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                             Artificial  PhET Lab   Tinkercad   Micro:bit     VEX        Digital    School Fee
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           Shaping Young Minds Through Artificial Intelligence & Robotics




                                                              |
                                                  Content   Team Orange]]></page><page Index="4"><![CDATA[Published by:
                      Orange Education (An Imprint of Orange House Pvt Ltd)
                      9, Daryaganj, New Delhi-110002
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                      Published and Distributed by Orange House Pvt Ltd




                            Preface


                      In today’s rapidly changing world, Artificial Intelligence and Robotics are transforming the way we live, learn and connect. As
                      the need for future-ready skills continues to grow, it is vital to equip students with the knowledge and tools required to excel
                      in Science, Technology, Engineering and Mathematics (STEM).
                      AI RoboGenius offers an engaging learning journey that combines the fascinating worlds of Artificial Intelligence, robotics and
                      STEM education. Designed to spark curiosity and creativity in young learners, the series blends hands-on exploration, critical
                      thinking and real-world problem-solving to nurture innovation and logical reasoning.
                      The projects in AI RoboGenius can be carried out through both online simulations and physical kits, giving students the
                      flexibility to learn in digital or hands-on environments. Whether coding, designing or building, learners develop a strong
                      understanding of STEM and AI concepts while applying them in practical, meaningful ways. The series focuses on experiential
                      learning, ensuring that students not only grasp theoretical knowledge but also learn to use it effectively in real-life situations.
                      Aligned  with international  educational  standards,  AI  RoboGenius supports  the development  of essential  skills such as
                      analytical thinking, computational reasoning and creative problem-solving. By the end of the series, students will gain the
                      confidence to innovate, design intelligently and transform their ideas into tangible outcomes.
                      We welcome constructive suggestions to help enhance AI RoboGenius further and make the learning experience even more
                      inspiring and impactful.
                                                                                                                 —Author]]></page><page Index="5"><![CDATA[TRULY ALIGNED WITH THE 9 PRINCIPLES OF NCFSE-2023


                       The books follow the principles of curriculum, values and discipline while promoting inclusion
                        and using real-life contexts. They use clear language, creative pedagogy and technology to
                     make learning joyful. The presentation is engaging, supporting every child’s meaningful learning.

                                                      st
                                                    21  CENTURY SKILLS

                      Learning Skills (4Cs)            Literacy Skills (IMT)           Life Skills (FLIPS)

                            Critical Thinking                 Information Literacy           Flexibility
                            Creativity                       Media Literacy                   Leadership & Responsibility
                            Communication                     Technology Literacy            Initiative
                            Collaboration                                                     Productivity & Accountability
                                                                                             Social Interaction










                                                      DIGITAL RESOURCES


                 FLIPBOOK


















                                                                                        DIGITAL SIMULATIONS]]></page><page Index="6"><![CDATA[ABOUT THE SERIES




                                                        Learning Resources




                                Ask AIRO


                              Engages with curiosity-based
                              questions and explores insightful
                              answers.

                                                                                    Smart Spark

                                                                                  Discovers short and amazing facts
                                                                                  that spark curiosity and inspire
                                                                                  deeper learning.

                                Ethics Edge

                              Focuses on understanding, practising
                              and applying ethical values and
                              responsible behaviour in robotics.

                                                                                    Challenge Chain

                                                                                  Tackles step-up challenges that
                                                                                  introduce exciting twists and build
                                                                                  problem-solving skills.

                                Ecotech Energy

                              Explores how robots contribute to
                              environmental sustainability through
                              innovation and smart solutions.

                                                                                     Career Climb

                                                                                  Learns about diverse career paths
                                                                                  and how individuals shape and
                                                                                  grow their careers in robotics.
                                 Bot Bits


                              Revisits key points of the chapter
                              through a brief summary for easy
                              recall and understanding.]]></page><page Index="7"><![CDATA[Assessment Resources




                                                                                           Robo Check


                                                                             Reinforces understanding and
                                                                           improves recall of key concepts
                                                                                 through focused practice.

                                     Visual Vault

                      Enhances observation and visual
                  interpretation skills through engaging
                       and thought-provoking visuals.

                                                                          Competency-Based Questions

                                                                                 Builds the ability to apply
                                                                            knowledge effectively and think
                                                                           critically in real-world contexts.

                                      Case Study

                    Builds understanding of real-world
                        applications and highlights the
                       industry relevance of concepts.

                                                                                    Capstone Project

                                                                       Encourages meaningful connections
                                                                          between learning and real-world
                                                                             problem-solving experiences.

                                  Maker Mission

                     Fosters creativity, innovation and
                   hands-on application skills through
                     engaging and practical activities.
                                                                                       Innovation Lab


                                                                        Revises concepts through practical
                                                                               lab activities to understand
                                                                                     and build confidence.]]></page><page Index="8"><![CDATA[CONTENTS





                       1.     STAGES OF AI PROJECT CYCLE                                                         7
                                Overview of Stages of AI Project Cycle           Problem Scoping
                                Data Acquisition                                 Data Exploration
                                Modelling                                        Evaluation—The Testing Stage
                                Deployment


                       2.     THE WORLD OF ROBOTS: ETHICS, LAWS AND FUTURE                                      31

                                 Understanding Robots                                    Generations of Robots
                                  Ethics in Robots                                       Robotics
                                  Laws of Robotics                                       Real-world Robots
                                  The Future of Robotics and Robots



                       3.     ROBOT AS A SYSTEM                                                                42
                                 Building Blocks of Robots
                                  Recognising and Understanding Robots

                                  Robots Based on Size and Design


                       4.     INTRODUCTION TO GOOGLE TEACHABLE MACHINE                                         51
                                 Google Teachable Machine
                                 Working of Google Teachable Machine

                                 Creating a Project using Google Teachable Machine


                       5.     BUILDING PROJECTS WITH GOOGLE TEACHABLE MACHINE                                  67
                                 Google Teachable Machine Projects                       Image Projects
                                  Audio Projects                                         Pose Projects


                       6.     SENSORS, LOGIC AND MAZE NAVIGATION                                               86

                                VR Robot’s Sensors                                     Magnet in VR Programming
                                Console in VR Programming                              Logic in VR Programming
                                Programs Using Different Playgrounds


                              CAPSTONE PROJECT                                                                 101


                              COMPONENTS LIST                                                                 103


                              YOUNG INNOVATORS                                                                104]]></page><page Index="9"><![CDATA[1











                                                                                              Stages of AI



                                                                                         Project Cycle






                 LEARNING OBJECTIVES


                    Overview of Stages of AI Project Cycle        Problem Scoping
                   Data Acquisition                               Data Exploration
                   Modelling                                      Evaluation—The Testing Stage
                   Deployment



                         Hey AIRO, I was reading about the stages of the
                         AI Project Cycle and it sounds really interesting.




                                                           It surely is, AVINA. Each stage plays an important
                                                                        role in building a successful AI model.





                          Can you tell me how these
                          stages actually work together?



                                                                    First, we define the problem, collect data,
                                                                       explore it and then build the AI model.






                          That sounds exciting. What
                          happens after building the model?


                                                      We evaluate its performance and then deploy it. Each
                                                           stage builds on the last for a complete AI solution.







                                                                                 Stages of AI Project Cycle
                                                                                                                    7]]></page><page Index="10"><![CDATA[OVERVIEW OF STAGES OF AI PROJECT CYCLE

                  By defining the problem, gathering relevant data, analysing it, building a model and evaluating its
                  performance, the AI project cycle helps ensure your project meets its objectives. This cycle offers
                  a structured roadmap for creating effective AI solutions.
                  The following figure shows the stages of AI Project Cycle:


                                                                Problem
                                                                Scoping


                                                                                   Data
                                             Deployment
                                                                                Acquisition








                                                                                   Data
                                             Evaluation
                                                                               Exploration



                                                               Modelling



                  The description of the stages of AI project cycle is as follows:

                     Problem Scoping: This is the first step where the problem is identified and clearly defined using
                     the 4Ws—Who, What, Where and Why. It helps in understanding the issue and planning how AI
                     can solve it.
                     Data Acquisition: In this stage, relevant raw data (text, images, videos, etc.) is collected from
                     sources like the Internet, books or surveys to support the project.

                     Data Exploration:  The collected  data  is  analysed  and  visualised  using  statistical  tools  to
                     understand patterns, trends and insights.
                     Modelling: Suitable algorithms are selected to build and test models that can solve the problem
                     effectively. Different models are compared to choose the best fit.
                     Evaluation: The model is tested to check its performance. If it fails to meet the goals, data or
                     the model may be adjusted. Once ready, the model proceeds to deployment.

                     Deployment: The final model  is  implemented in a real-world  environment  with ongoing
                     monitoring and maintenance to ensure continued success.
                  These stages help in systematically address challenges from the initial problem identification.
                  It ensures that data collected is relevant and complete, which contributes to building a reliable
                  model and gaining meaningful insights.
                  By following the AI project cycle, you increase the chances of developing AI solutions that are not
                  only functional but also practical and impactful in real-world scenarios.



                                 AI RoboGenius - VIII
                  8]]></page><page Index="11"><![CDATA[Scenario:

                 Imagine using our AI skills to explore and discover habitable planets, just like in science fiction
                 movies. How could AI help analyse astronomical data, predict conditions on other planets or even
                 simulate ecosystems that could support life on distant worlds?
                 Let’s try to solve a space-related challenge using the AI Project Cycle. Suppose we want to find an

                 ‘Earth-like exoplanet’.
                 Finding an Earth-like exoplanet

                    An exoplanet is any planet located outside our solar system.
                    Our goal is to identify a planet similar to Earth that exists far away from us.

                    Some examples of Earth-like exoplanets are Gliese 667Cc and Kepler-69c.

                                                                                               21 st
                                                                                              Century   #Media Literacy
                                                                                               Skills
                           Video Session

                     Watch the video of “Exoplanets: Weird, Wondrous Worlds” at the given link:

                     https://www.youtube.com/watch?v=4IXYp9Fse44 or scan the  QR code  and
                     answer the following question:

                     Name different types of exoplanets discussed in the video.








                 PROBLEM SCOPING

                 Problem scoping is the first and most important step in the AI project cycle. It involves identifying
                 a real-world issue that can be addressed using AI. This step requires not only spotting a problem
                 but  also  imagining  how AI can help solve it.  Often, we are surrounded  by  problems—big  or
                 small—but we may overlook them because they’ve become a normal part of our lives. Problem
                 scoping  encourages  us  to  observe  our surroundings  carefully and  define  a clear goal  for the
                 AI solution.

                 The Problem Scoping phase includes the following steps:
                  STEP - 1   Identifying the problem: Clearly define the specific issue your AI project is intended
                              to solve.

                  STEP - 2   Understanding  the context:  Study  the  background,  causes  and  impact  of the
                              problem to gain a complete understanding.

                  STEP - 3   Defining goals and objectives: Set clear, achievable goals that align with the problem
                              and guide the AI solution’s direction.
                  STEP - 4   Identifying stakeholders: Recognise all individuals or groups affected by the problem
                              or involved in its solution, including those who can contribute valuable insights.



                                                                                 Stages of AI Project Cycle
                                                                                                                    9]]></page><page Index="12"><![CDATA[STEP - 5   Setting project boundaries: Clearly outline the scope of the project, specifying what
                               will and will not be addressed within the AI solution.

                  4Ws Problem Canvas in AI Project Cycle

                  In the  Problem  Scoping  stage  of the  AI Project  Cycle,  the  4Ws Problem Canvas is  a  helpful
                  tool used to break down and understand the problem step by step. It focuses on four essential

                  questions—Who, What, Where and Why—to clearly define the problem and its impact.


                          Who                       What                      Where                      Why


                  Who?

                  This section helps identify the stakeholders—the people who are directly or indirectly affected by

                  the problem. Understanding who faces the problem allows us to design an AI solution that truly
                  meets their needs.

                     Who are the stakeholders?
                     What do we know about them?

                  What?

                  Here, we define the exact problem. This includes describing its nature and backing it up with

                  evidence, such as reports, media coverage or survey data.
                     What is the problem?

                     How do we know it’s a problem?

                  Where?

                  This part  focuses  on the  context and  location where the  problem  occurs.  It explores  the
                  environment or situation in which stakeholders experience the issue.

                     What is the context or situation in which the problem appears?

                     Where is the problem happening?

                  Why?

                  Finally, we explore  the  purpose of solving the  problem.  This section  outlines  how the
                  solution  will benefit  the stakeholders  and improve their  condition,  thus  giving  meaning  to
                  the project.

                     Why is the solution valuable for the stakeholders?

                     How will it improve their lives or address the issue effectively?
                  By filling out the 4Ws Canvas, we create a strong foundation for the AI project, ensuring that

                  every next stage—data collection, modelling and deployment—is aligned with real-world needs
                  and goals.

                                 AI RoboGenius - VIII
                 10]]></page><page Index="13"><![CDATA[Activity

                  Problem Statement

                  “Inadequate sleep during examination time leads to poor academic performance and health
                  issues among students.”

                  4Ws Canvas for Problem Scoping
                     Who? (Stakeholders)

                         Students are directly affected due to fatigue and poor performance.
                         Teachers are indirectly affected by declining academic results.

                         Parents are concerned about their children’s health and academic stress.
                     What? (Nature of the Problem)

                         Students show low performance in exams due to sleep deprivation.

                         Teachers observe reduced attentiveness and participation in class.
                         Lack of sleep impacts students’ mental and physical well-being.

                     Where? (Location/Context)
                         At home: students stay up late studying or using devices.

                         In school: teachers notice reduced focus.
                         Examination hall: students feel tired and underperform.

                     Why? (Purpose of Solving)

                         To enhance students’ focus and energy during exams.
                         To improve academic performance through better sleep routines.

                         To maintain students’ mental and physical health.

                         To support time management and stress reduction.



                               Task                                                   21 st  Century   #Leadership & Responsibility
                                                                                          Skills

                    Case: Substantial Electricity Bill

                    You notice that the electricity bill for your school is very high. Your goal is to reduce this bill. To
                    do this, use the 4Ws Problem Canvas to solve the issue.


                 Problem Statement Template

                 After filling the 4Ws problem canvas, you now need to summarise all the canvas into one
                 template. The problem statement template helps us to put together all the key points into
                 a single template for future reference. Problem statement template with space to fill details
                 according to your goals:

                                                                                 Stages of AI Project Cycle
                                                                                                                    11]]></page><page Index="14"><![CDATA[Our                                         [stakeholder(s)]                        Who









                    Has / Have a problem that                [issue, problem, need]                    What









                    When / While                              [context, situation]                     Where









                    An ideal solution would be           [benefit of solution for them]                 Why











                                 Task                                                         Century   #Critical Thinking
                                                                                                21 st
                                                                                               Skills

                     Managing Traffic Congestion in Urban Areas

                     Many cities around the world face daily traffic jams, especially during office hours. The growing
                     number of vehicles, limited road space and poor traffic management systems cause long
                     delays, fuel wastage and frustration among commuters. Despite efforts like traffic signals,
                     flyovers and alternate routes, congestion continues to rise due to unpredictable vehicle flow
                     and poor route planning.
                     Let us explore the issue further using the 4Ws Problem Canvas Template to understand the
                     problem better.




                  DATA ACQUISITION

                  Data Acquisition is the foundation of a successful AI project. In this stage, relevant data
                  is identified, collected and organised for further analysis and modelling. Since AI systems
                  learn from data, the quality, quantity and variety of data directly impact how accurately the
                  AI system can perform.




                                 AI RoboGenius - VIII
                 12]]></page><page Index="15"><![CDATA[Without proper data acquisition, even the most advanced algorithms will produce poor results.
                 Hence, this stage ensures the AI model has a reliable and representative dataset that reflects
                 real-world conditions.

                 Following are the points that shows why is data acquisition important:

                    It provides the raw material for building AI models.
                    Helps in identifying patterns, trends and relationships.

                    Influences the accuracy, fairness and efficiency of AI outcomes.
                    Aids in detecting biases and making the system more inclusive.




























                 Types of Data Collected

                 The success of any AI system depends heavily on the quality and variety of data collected during
                 the Data Acquisition stage. Different problems require different types of data and each data type
                 brings unique value and challenges.


                                                            Types of Data









                                        Textual        Numerical         Visual          Audio
                                         Data            Data            Data             Data


                 Textual Data

                 Textual data consists of information presented in written or printed form—typically as letters,
                 words, sentences or paragraphs.






                                                                                 Stages of AI Project Cycle
                                                                                                                    13]]></page><page Index="16"><![CDATA[Examples:

                     Customer reviews on e-commerce platforms              Emails and SMS messages
                     News articles or blog posts                           Chat transcripts from customer support
                  Numerical (Quantitative) Data

                  This type of data is expressed in numbers and is typically used for statistical and mathematical
                  analysis.

                           ID               Value_A             Value_B             Value_B            Category
                         1001                 25.5                120                Alpha             2024-01-15

                         1002                 30.1                155                 Beta             2024-01-16
                         1003                 18.7                 98               Gamma              2024-01-17
                         1004                 42.0                200                Alpha            2024-01-18
                         1005                 29.9                130                 Delta           2024-01-19

                         1006                 35.6                170                 Beta            2024-01-20
                         1007                 22.3                110               Gamma             2024-01-21
                         1008                 48.2                230                 Delta           2024-01-22
                         1009                 15.0                 75                Alpha            2024-01-23
                         1010                 50.8                250                 Beta            2024-01-24



                                 AI RoboGenius - VIII
                 14]]></page><page Index="17"><![CDATA[Examples:
                 Age, income, temperature, height

                 Sales figures, number of products sold, website clicks
                 Exam scores, financial records
                 Visual Data (Image and Video)

                 Visual data includes images, pictures and videos that capture the appearance of the world.

































                 Examples:

                 Photos from social media platforms
                 Surveillance footage

                 MRI and X-ray images in healthcare
                 Traffic camera footage

                 Audio Data

                 Audio data refers to sounds recorded through microphones or other recording devices.























                                                                                 Stages of AI Project Cycle
                                                                                                                    15]]></page><page Index="18"><![CDATA[Examples:
                  Voice commands (e.g., “Hey Google”)

                  Music and soundtracks
                  Phone conversations
                  Audiobooks and podcasts

                  Characteristics of Good Data

                  To ensure effective use in AI modelling, data must be:
                  Relevant: Closely related to the problem being solved.

                  Accurate: Free from errors and inconsistencies.
                  Complete: All necessary fields are filled.

                  Timely: Up-to-date and reflects current conditions.
                  Balanced: No over-representation or under-representation of any group (to avoid bias).
                  Formatted: Structured correctly for easy analysis (e.g., tabular, JSON, XML, etc.)

                  Sources of Data

                  Data for any project, including AI projects, can be collected from two main sources: Primary and
                  Secondary sources. Understanding these sources helps ensure the data is reliable and suitable
                  for analysis.
                  Primary source of data

                  Primary data is original data collected directly from the source for the first time. This data is
                  specifically gathered to address the research problem or project requirements and has not been
                  previously published or used by others.



                                                        Primary source of data








                         Surveys                    Interviews           Observations                  Experiments
                      Questionnaires                One-on-one         Systematic recording           Controlled tests
                      distributed to a                or group           of behaviours or             where variables
                      selected group of             conversations         events as they             are manipulated to
                    people to gather their          conducted to         naturally occur,            observe effects and
                    opinions, behaviours           obtain in-depth      without interference.           outcomes.
                      or preferences.               information.

                  Some advantages of Primary data are as follows:

                  Highly relevant and specific to the problem.

                  Control over the data collection process, ensuring accuracy and reliability.
                  Can provide up-to-date and real-time information.



                                 AI RoboGenius - VIII
                 16]]></page><page Index="19"><![CDATA[Some disadvantages of Primary data are as follows:

                 Often time-consuming and expensive.
                 Requires careful planning and design to avoid biases.
                 Secondary source of data

                 Secondary  data  is data that  has already  been  collected,  processed  and published  by  other
                 individuals or organisations. Researchers use this data for new analysis or to supplement primary
                 data.
                 Examples of secondary data sources include:

                 Books and academic journals: Published research studies and scholarly articles.

                 Government reports: Census data, economic statistics, health records and official publications.
                 Websites and online databases: Public datasets, industry reports, open data portals like Kaggle
                 or WHO.

                 Newspapers and magazines: Articles and reports on current events and trends.

                                                                                             •   UCI is a collection
                                           •   Countries like                                   of databases,
                                              Australia, EU, India,                             domain theories and
                                              New Zealand and                                   data generators in
                                              Singapore are openly
                                              sharing datasets on    Dataset Search             collaboration with
                                                                                                the University of
                                              various portals
                                                                                                Massachusetts


                 •   Kaggle is an online                            •   This is a toolbox by
                    community of data              .gov                Google that can       UCI
                    scientists where you                               search for data by
                    can access different       datasets                name                   Machine Learning Repository
                    types of data



                 Some advantages of secondary source data are as follows:

                 Easily accessible and often free or low-cost.

                 Saves time as data collection is already done.
                 Useful for historical or trend analysis.

                 Some disadvantages of secondary source data are as follows:

                 May not perfectly fit the current research question.
                 Possible issues with accuracy, reliability or outdatedness.

                 Lack of control over data quality and collection methods.
                 A list of government websites for data collection:








                                                                                 Stages of AI Project Cycle
                                                                                                                    17]]></page><page Index="20"><![CDATA[S. No.           Website Name                      URL                       Purpose

                            National Statistical Office                             National surveys, statistics on
                      1.                                      mospi.gov.in
                            (NSO)                                                   economy, population, health
                            Open Government Data (OGD)                              Public access to datasets from
                      2.                                      data.gov.in
                            Platform                                                different departments
                                                                                    Population data, demographic
                      3.    Census of India                   censusindia.gov.in
                                                                                    statistics
                                                                                    Banking, inflation, GDP, interest
                      4.    Reserve Bank of India (RBI)       rbi.org.in
                                                                                    rates
                                                                                    Policy reports, indices like SDG,
                      5.    NITI Aayog                        niti.gov.in
                                                                                    innovation, health

                      6.    Ministry of Education (AISHE)     aishe.gov.in          Higher education statistics




                                 Brainy Fact


                     Google processes over 8.5 billion searches each day and image searches make up a
                     significant portion — with hundreds of millions to over a billion image searches performed
                     daily worldwide.





                                 Task

                                                                                                       21 st
                                                                                                     Century   #Initiative
                                                                                                      Skills
                     Case: Substantial Electricity Bill (Continued…)
                     Start by collecting data from teachers about how often students forget to switch off
                     lights and fans in the classrooms. You can also gather similar information from students and
                     their parents.





                  DATA EXPLORATION

                  Data exploration is an important step where large amounts of data are examined to find useful
                  patterns, trends and connections using simple statistics and data visualisation tools. By turning
                  raw data into pictures like charts and graphs, data scientists can better understand and learn
                  from it. This process helps to reveal hidden details in the data and spot any unusual or incorrect
                  information. It also sets the stage for making smarter decisions in the next steps of an AI project.
                  It helps in:
                  To study and organise data so that it is shown in simple and clear visual formats.

                  Presenting data visually makes it easier for people to understand and use the information.




                                 AI RoboGenius - VIII
                 18]]></page><page Index="21"><![CDATA[Providing clear data presentation helps in making faster and better decisions.
                 Displaying data in many different ways, such as charts, graphs, tables and diagrams.

                 Showing data clearly also helps to identify errors or unusual information quickly.

























                                                             Column Chart
























                                                               Pie Chart























                                                              Scatter Plot



                                                                                 Stages of AI Project Cycle
                                                                                                                    19]]></page><page Index="22"><![CDATA[Data Visualisation Tools

                  Some commonly used data visualisation tools are:
                  Microsoft  Excel: A manual tool  from Microsoft  offering basic  charts and graphs for small,
                  organised datasets. It is not suitable for very large data.
                  Tableau  Public: A free,  widely  used  software that  creates  interactive  data  visualisations  and
                  supports very large datasets.

                  Microsoft Power BI: A powerful and free data visualisation tool by Microsoft that helps in analysing
                  and presenting data effectively.
                  Google Data Studio: Part of Google Marketing Platform, this tool allows you to create multiple
                  custom views and reports from your data.
                  Plotly: An open-source graphing library for creating high-quality interactive charts in Python, R
                  and JavaScript.

                  Zoho Analytics: A cloud-based tool offering drag-and-drop interfaces to create detailed reports
                  and dashboards.
                  Infogram: An easy-to-use tool for creating infographics, reports and interactive charts for online
                  sharing.


                     Activity


                   Collecting and Exploring Data!
                   In this activity, you will learn how to collect, look at and understand data. Data collection means
                   gathering information from different places, like surveys or online sources. Data exploration
                   means  organising  and  showing  this  information  in  simple  ways  to  find  useful  details  and
                   patterns. By the end of this activity, you will see how data can help answer questions and tell
                   stories.

                   First, we will collect data. This could be from a list, a website or by asking people questions.
                   After collecting the data, we will organise and check it carefully. This means fixing any mistakes,
                   sorting it and looking for patterns or trends. You will also learn how to make pictures like charts
                   and graphs that make the data easy to understand.

                   Doing this will help you learn important skills for making good decisions based on facts. These
                   skills are useful when working with numbers, words or pictures.

                   Let’s start and discover how powerful data can be!
                   How to Do the Activity

                     STEP - 1   Collecting Data

                                 Ask every student in the class for this information:

                                    Name                         Height                         Weight
                                    Age                          Address                        Favourite hobby





                                 AI RoboGenius - VIII
                 20]]></page><page Index="23"><![CDATA[Write all this information in a table, with each piece of information as a column.
                               You can use the sample table given to you.


                       Name          Height       Weight          Age            Address         Favourite Hobby





















                   STEP - 2   Exploring Data

                               Now that we have the data, we can learn a lot from it.
                                 Calculate the Body Mass Index (BMI) for each student using their height and weight.
                               BMI is a number that shows if someone is underweight, normal or overweight.

                                                                Weight (kg)
                                                         BMI =
                                                                      2
                                                                Height  (m) 2
                               Make a pie chart to show how many students fall into each BMI group:


                                   Underweight: BMI less than 18.5

                                   Normal weight: BMI from 18.5 to 24.9

                                   Pre-Obesity: BMI from 25 to 29.9

                                   Obesity: 30 and above




























                                                                                 Stages of AI Project Cycle
                                                                                                                    21]]></page><page Index="24"><![CDATA[Make bar graphs to compare one pair of information below (you can pick one):
                                    Age and Favourite Hobby

                                    Place they live and BMI group

                                    Height and Weight























                                 This activity will help you practice how to collect correct data, organise it, find
                                 important information and show it in pictures that everyone can understand easily.
                                 It will also show you how data can help answer questions and make decisions in
                                 everyday life.



                  Types of AI Techniques

                  Various types of AI techniques can be chosen according to project requirements. The significant
                  techniques are:

                  Supervised Learning: In this technique, the data sets are labelled. For example, to determine
                  whether an email is spam, a model is trained on a dataset of labelled emails (both spam and
                  non-spam) to learn how to classify new and unseen emails.

                  Unsupervised Learning: Unsupervised learning occurs when a computer identifies patterns in
                  data independently to discover hidden relationships. For example, we provide computer images
                  of cats and dogs without labelling them and the computer identifies them by recognising features

                  such as their ears, fur, etc.
                  Reinforcement Learning: In this technique, the model learns from feedback provided in the form

                  of rewards or penalties. For example, consider a game where a child needs to throw a ball into a
                  basket. In this scenario, the child acts as the agent and learns that when the ball successfully goes
                  into the basket, they earn a point. Conversely, a negative score is recorded if the ball misses the

                  basket. Through this process, the child improves their performance and learns to earn points by
                  successfully putting the ball in the basket.





                                 AI RoboGenius - VIII
                 22]]></page><page Index="25"><![CDATA[Task                                                           Century   #Creativity
                                                                                               21 st
                                                                                                21 st
                                                                                                     #Creativity
                                                                                              Century
                                                                                                     #Social Interaction
                                                                                               Skills
                                                                                                     #Social Interaction
                                                                                               Skills
                    Case: Substantial Electricity Bill (Continued…)
                    Part 1: Collect the following information from every student:
                    Name
                    Number of electrical appliances used daily at home

                    Average number of hours electricity is used at home daily
                    How often lights and fans are left on unnecessarily at home
                    Is the student responsible at school? (Yes/No)
                    Is the student responsible at home? (Yes/No)
                    Part 2: Group the students into these categories:

                    Responsible Both at School and Home
                    Responsible at School Only

                    Responsible at Home Only
                    Not Responsible at Either
                    Create Charts:
                    Pie Chart: For Responsibility Categories

                    Bar Graph: For Student Name vs Electricity Usage Hours
                    Column Graph: For Number of Appliances vs Responsibility
                    After analysing the data, you notice a pattern: students who are not responsible at school
                    also tend to leave lights and fans on at home.

                    Answer these questions:
                    1.  How many students are responsible at both school and home?
                    2.  Who uses the most appliances daily?

                    3.  What pattern do you notice between responsibility at school and at home?
                    4.  What suggestions do you have to reduce electricity usage both at school and at home?




                 MODELLING

                 Data Modeling refers to the process of developing decision-making algorithms that are trained
                 on the dataset gathered during the data acquisition phase, following the problem defined in the
                 problem-scoping phase. These algorithms learn to detect particular patterns within the data.
                 Once the data has been visualised and patterns identified, the focus shifts to building the AI model
                 by applying these algorithms. This can be achieved either by creating new models or by leveraging
                 existing AI models.

                 AI modelling involves creating algorithms called models that are designed to learn from data and
                 make intelligent decisions or predictions. This process essentially means programming machines


                                                                                 Stages of AI Project Cycle
                                                                                                                    23]]></page><page Index="26"><![CDATA[to simulate human thinking and problem-solving abilities. By developing these models, we enable
                  AI systems to perform tasks that require reasoning, understanding and adaptability.
                  AI modelling techniques can be broadly classified into two approaches, namely, rule-based and
                  learning-based.

                  Rule-Based AI

                  Rule-based approach is based on a set of rules and a set of facts already fed to the machine to
                  generate the desired output. These models can operate with simple basic information and data.
                  The relationship or patterns in the data is defined by the developer.
                  IF X happens THEN do Y

                  Examples of rule-based AI
                  Example 1: If IT RAINS Then TAKE AN UMBRELLA
                  Example 2: If TRAFFIC LIGHT TURNS RED Then STOP THE CAR

                  Let us understand the concept of a Rule-Based AI system using a given scenario.
                  You have a dataset containing 100 images of chairs and 100 images of tables. To train your
                  machine, you provide this data and label each image as either a chair or a table. When you test
                  the machine with a new image of a chair, it compares this image to the labelled training data and
                  identifies it as a chair based on those labels. This method is known as a rule-based approach,
                  where the rules are the labels assigned to the training data.



                                                                                        Rule-based Approach


                                                                  Labelled Datasets



                                                                                               Model
                                                                Used to Train Machine        Used to Test Machine














                                                                        Output                         Training Data






                              Machine Identifies the Image as Table                            Testing Data



                  Based on various guidelines, we can make different choices.



                                 AI RoboGenius - VIII
                 24]]></page><page Index="27"><![CDATA[Now, let’s explore another type of learning.
                 Instead of explicitly programming rules such as "if this happens, then do that", we let the system

                 discover patterns or rules by itself that help solve the problem.
                 How do humans make decisions?
                 For example, imagine we are preparing a glass of lemonade, but we don’t know exactly how

                 much sugar to add.
                 Instead of following a strict rule, we taste the lemonade after adding some sugar, then adjust the
                 amount based on how sweet it is. Over time, we learn the best amount of sugar for our taste by

                 trying, observing and adapting.
                 This process of learning from experience rather than following fixed rules is similar to how some
                 AI systems learn and improve.


                   Time of lemonade making              Amount of sugar added                Taste of lemonade
                  First time                      Very little sugar (quarter teaspoon)    Not sweet enough
                  Second time                     Too much sugar (two teaspoons)          Too sweet

                  Third time                      Moderate sugar (one teaspoon)           Just the right sweetness
                  Fourth time                     Moderate sugar (one teaspoon)           Just the right sweetness


                                                                                             21 st
                                                                                            Century   #Media Literacy
                                                                                             Skills
                           Video Session

                     Watch the video of "How does ARTIFICIAL INTELLIGENCE work?" at the
                     given link:
                     https://www.youtube.com/watch?v=fLcZRDiQyNY OR  scan the  QR code
                     and answer the following questions:

                     1.  How do characters in the video decide which direction to take?



                     2.  What do the different colours in the video represent?



                     3.  How does Rule-Based AI use predefined rules to reach a destination?







                 Learning-Based AI
                 A learning-based approach is when a machine learns by itself from the data it receives, instead

                 of following fixed rules made by a person. When the data is unlabelled or random, the machine
                 looks for similarities and groups similar data together.


                                                                                 Stages of AI Project Cycle
                                                                                                                    25]]></page><page Index="28"><![CDATA[This way, it can find hidden patterns or trends without anyone telling it exactly what to do. This
                  approach is useful because sometimes it’s hard to write clear rules for complicated or messy
                  data. The machine’s ability to discover patterns helps solve problems that rule-based systems

                  cannot handle easily.
                  Let  us  understand  the  concept  of  a  learning-based  AI approach  using  a  simple  example
                  of fruit  images.  In this  method,  the  AI model  is given  unlabelled data—in this  case,  various

                  fruits like  apples,  bananas, grapes  and pears—without  any names  or predefined  categories.
                  The machine does not know what each fruit is called or how it should be grouped. Instead of
                  following fixed rules, the AI analyses the visual features of the fruits such as shape, colour and

                  size  to  find  patterns.  It  then  learns from these  patterns and  clusters  similar  items  together.
                  For example, it groups all bananas together based on their long, curved shape and yellow colour
                  and clusters apples together based on their round shape, even if the colours vary. This process
                  is known as unsupervised learning, where the machine identifies categories or groups by itself
                  without human-provided labels.

                  The following image effectively demonstrates how a learning-based AI model can independently

                  observe, analyse and group data based on the features it detects.


                                                                                     Learning Approach

                                                                    Unlabelled Data


                                                                                         Model
                                                                   Used to Train Dataset

                                             Unlabelled Data                                  Output













                                                Output is clustered based on patterns observed by the machine:
                                      Left is based on roller stakes, Middle is based on Ice Skates and Right is based on Inline skates.




                                 Task                                                        Century   #Creativity
                                                                                              21 st
                                                                                              Skills  #Technology Literacy
                     Case: Substantial Electricity Bill (Continued…)

                     You begin designing a sensor and alarm system that detects movement in the room. If there is
                     no movement, the alarm will sound to remind someone to turn off the lights and fans.




                                 AI RoboGenius - VIII
                 26]]></page><page Index="29"><![CDATA[EVALUATION—THE TESTING STAGE

                Evaluation is one of the most critical stages of an AI project. This phase is an important step
                where we check how well the AI model works to see if it meets the goals set at the start. If the

                model does not perform as expected, changes may be needed. Once the developer is happy
                that the model works well and fits the project’s needs, the AI project moves to the next phase
                called deployment.



                               Task                                                          21 st
                                                                                            Century
                                                                                             Skills  #Critical Thinking
                   Case: Substantial Electricity Bill (Continued…)
                   After installing the sensor and alarm system in some classrooms, gather the following data:

                   Reduction in electricity usage compared to the period before the system was installed
                   Number of times the alarm went off and what actions were taken

                   Feedback from teachers and students on how effective and easy to use the system is

                   A comparison of the costs of installing and maintaining the system versus the money saved
                   on electricity
                   Any technical problems or false alarms during the trial period

                   Use this data to decide if the system effectively reduces electricity usage and if it can be used
                   in all classrooms. Use this evaluation to improve the system before fully implementing it.




                DEPLOYMENT

                In this phase, the best model is put into real use. It is added to the actual system where people
                can use it in everyday work. A system is also set up to watch the model’s performance regularly,
                making sure it keeps giving correct and reliable results. If problems come up, like changes in data
                or lower performance, the model is fixed and updated. The goal is to keep the model working well
                over time and make improvements as needed.


                               Task                                                          21 st
                                                                                            Century
                                                                                            Skills  #Information Literacy
                   Case: Substantial Electricity Bill (Continued…)

                   Start by installing the solution (the app and sensors) in a few classrooms. After testing and
                   reviewing the results, including the cost of installation and the reduction in electricity use,
                   check if the system helps reduce the bill. If it does, roll out the system to all classrooms. Collect
                   feedback to improve the system based on how it is used in real-time.







                                                                                 Stages of AI Project Cycle
                                                                                                                    27]]></page><page Index="30"><![CDATA[4.  What is the first step in the AI project cycle?

                                                                                                                                                            (i)  Data Acquisition                     (ii)  Problem Scoping
                     «   AI project cycle offers a structured roadmap for creating effective AI solutions.
                                                                                                                                                          (iii)  Modelling                           (iv)  Evaluation
                     «   Problem  scoping  involves identifying  a  real-world issue  that  can  be  addressed
                         using AI.                                                                                                              5.  Which data visualisation tool is NOT mentioned in the chapter?
                     «   The Problem Scoping phase includes identifying the problem, understanding the context, defining                                    (i)  Microsoft Excel                     (iv)  Tableau Public
                         goals and objectives, identifying stakeholders, setting project boundaries.

                     «   The 4Ws Problem Canvas is a helpful tool used to break down and understand the problem step                                      (iii)  Microsoft Power BI                  (iv)  MATLAB
                         by step.
                                                                                                                                            B.  Write ’T’ for true and ’F’ for false.
                     «   The problem statement template helps us to put together all the key points into a single template
                         for future reference.                                                                                                  1.   In data acquisition, the model is tested to check its performance.


                     «   Different problems require different types of data and each data type brings unique value and                          2.   Secondary data is data that has already been collected, processed and published
                         challenges.
                                                                                                                                                   by other individuals.
                     «   Data for any project, including AI projects, can be collected from two main sources: Primary and
                         Secondary sources.                                                                                                     3.  Different problems require different types of data.

                     «   AI modelling techniques can be broadly classified into two approaches, namely, rule-based and                          4.  Data exploration is the foundation of a successful AI project.
                         learning-based.
                                                                                                                                                5.   Tableau Public is a free, widely used software that creates interactive data
                                                                                                                                                   visualisations and supports very large datasets.


                                                                                                                                            C.  Fill in the blanks using the given hints:
                                                                                                                                                                    data is original data collected directly from the source
                                                            Robo Check                                                                          1.  for the first time.                                                               Hints

                                                                                                                                                                                                                                    Modelling
                                                                                                                                                2.                  refers to  sounds  recorded  through microphones  or
                  A.  Tick ( ) the correct option.                                                                                                                                                                                   Problem
                                                                                                                                                   other recording devices.                                                         statement
                      1.   When data is labelled, which learning technique is used?
                                                                                                                                                3.    The               template helps us to put together all the key points      Data acquisition
                                  (i)  Supervised learning                  (ii)  Unsupervised learning                                                                                                                             Audio data
                                                                                                                                                   into a single template for future reference.
                                                                                                                                                                                                                                     Primary
                                (iii)  Reinforced learning                (iv)  None of these                                                   4.  The fourth step in the AI project cycle is called              .

                      2.   Which AI technique learns  by making  decisions  and receiving  feedback  in the form of                             5.                  ensures the AI model has a reliable and representative
                         rewards or penalties?                                                                                                     dataset that reflects real-world conditions.

                                  (i)  Supervised learning                  (ii)  Reinforced learning
                                                                                                                                            COMPETENCY-BASED QUESTIONS
                                (iii)  Unsupervised learning              (iv)  Rule-based AI
                                                                                                                                            1.  Ria trains an AI model using labelled emails to identify spam. Kabir’s program studies animal
                      3.  The deployment of an AI model happens after which stage?                                                              pictures without labels and groups them by similarity. Tara builds a game where a robot
                                                                                                                                                learns by getting rewards or penalties. Which different AI techniques are used by Ria, Kabir
                                  (i)  Evaluation                           (ii)  Modelling
                                                                                                                                                and Tara?
                                (iii)  Data exploration                   (iv)  Problem scoping                                             2.  During a school project, Naman collects data on students’ study habits and exam scores.
                                                                                                                                                He then uses an AI program to find patterns and predict how much study time might lead to
                                                                                                                                                better results. Which Artificial Intelligence process is Naman using to train algorithms that can
                                                                                                                                                learn from data and make decisions or predictions?


                                 AI RoboGenius - VIII
                 28]]></page><page Index="31"><![CDATA[4.  What is the first step in the AI project cycle?

                                (i)  Data Acquisition                     (ii)  Problem Scoping

                               (iii)  Modelling                          (iv)  Evaluation

                    5.  Which data visualisation tool is NOT mentioned in the chapter?
                                (i)  Microsoft Excel                     (iv)  Tableau Public


                               (iii)  Microsoft Power BI                 (iv)  MATLAB

                 B.  Write ’T’ for true and ’F’ for false.

                    1.   In data acquisition, the model is tested to check its performance.


                    2.   Secondary data is data that has already been collected, processed and published
                        by other individuals.

                    3.  Different problems require different types of data.

                    4.  Data exploration is the foundation of a successful AI project.

                    5.   Tableau Public is a free, widely used software that creates interactive data
                        visualisations and supports very large datasets.


                 C.  Fill in the blanks using the given hints:
                    1.                  data is original data collected directly from the source

                        for the first time.                                                               Hints
                                                                                                         Modelling
                    2.                  refers to  sounds  recorded  through microphones  or
                                                                                                         Problem
                        other recording devices.                                                         statement

                    3.    The               template helps us to put together all the key points      Data acquisition
                        into a single template for future reference.                                    Audio data
                                                                                                         Primary
                    4.  The fourth step in the AI project cycle is called              .

                    5.                  ensures the AI model has a reliable and representative
                        dataset that reflects real-world conditions.


                 COMPETENCY-BASED QUESTIONS

                 1.  Ria trains an AI model using labelled emails to identify spam. Kabir’s program studies animal
                    pictures without labels and groups them by similarity. Tara builds a game where a robot
                    learns by getting rewards or penalties. Which different AI techniques are used by Ria, Kabir
                    and Tara?
                 2.  During a school project, Naman collects data on students’ study habits and exam scores.
                    He then uses an AI program to find patterns and predict how much study time might lead to
                    better results. Which Artificial Intelligence process is Naman using to train algorithms that can
                    learn from data and make decisions or predictions?


                                                                                 Stages of AI Project Cycle
                                                                                                                    29]]></page><page Index="32"><![CDATA[In Life                                                   Century   #Critical Thinking
                                                                                              21 st
                                                                                              Skills

                    1.   An online  store  uses  AI to  recommend  products,  but  customers  say  the  suggestions  are not
                       relevant. What steps in the AI project cycle would you follow to improve the recommendation
                       system?
                    2.   Think of a daily life problem in your home or school. Plan the first three steps of an AI project cycle
                       to solve it.




                                Deep Thinking                                          Century   #Critical Thinking
                                                                                         21 st
                                                                                        Skills  #Productivity & Accountability
                     1.   Your  school has installed  an AI system  that  automatically  turns off lights  and fans in empty
                        classrooms to save electricity. However, sometimes the system turns them off while students are
                        still working, causing inconvenience.
                         Using the AI Project Cycle, explain how you would identify the problem, collect and explore the
                        right data and improve the AI model to fix this issue.

                     2.   What problems could arise if you do not clearly define the problem before starting an AI project
                        to reduce food waste?




                                                                                                    21 st
                                                                                                  Century   #Creativity
                                                                                                   Skills
                                Lab


                         Form a group of five students to identify a problem that can be solved using AI techniques,
                         such as reducing food wastage in the school canteen. Prepare an AI project plan. Write each
                         stage with a suitable heading. You may use Google Docs or Word to accomplish the task.































                                 AI RoboGenius - VIII
                 30]]></page><page Index="33"><![CDATA[2







                                                                                             The World of


                                                                                         Robots: Ethics,



                                                                                      Laws and Future






                 LEARNING OBJECTIVES


                    Understanding Robots                           Generations of Robots
                    Ethics in Robots                               Robotics
                    Laws of Robotics                               Real-world Robots
                    The Future of Robotics and Robots



                          Hey AIRO, what’s today's lesson about?
                          I heard something about robots.



                                                                     Today, we’re learning about robots! But
                                                                         first, can you guess what a robot is?






                          Is it something that helps with
                          chores, like cleaning my room?


                                                                     Good guess! Robots can do much more.
                                                                           Some can even think on their own.







                          Wow, do robots
                          think like humans?


                                                                Not exactly. They follow instructions and can
                                                                        make decisions, but they’re not alive.







                                                                  The World of Robots: Ethics, Laws and Future
                                                                                                                    31]]></page><page Index="34"><![CDATA[Technology is changing faster than ever and everyone needs new skills to keep up. Digital tools
                  like smartphones, tablets and computers are now part of daily life, so learning to use them well is
                  important.

                  Robotics is the science of designing and building robots. Robots are machines that can perform
                  tasks usually done by humans, from simple toys to complex industrial systems.

                  In the future, robots will take over routine work like cleaning, repairing and manufacturing jobs.
                  Some will even be able to process information, analyse situations and make decisions.
                  Today’s students already know about robots from toys, films and cartoons. This makes learning

                  robotics easier and more exciting for them.


                  UNDERSTANDING ROBOTS

                  The National Robotics Association explains a robot as a machine that can be programmed to do
                  many different tasks. It is designed to move materials, tools or parts and can follow instructions
                  to complete various jobs. In simple terms, a robot is a machine that can work on its own or with
                  very little help from people. Robots can be programmed to repeat the same actions again and
                  again or to do work that might be dangerous or too hard for humans.































                  A robot can be understood through the following points:
                     A robot  is a machine  made  up of mechanical,  electrical  and sometimes  electronic  parts,

                     created to carry out specific tasks.
                     A robot can do jobs that are usually done by humans, such as lifting, carrying or handling
                     objects.

                     A robot can work autonomously without needing constant human control.

                     A robot can detect its surroundings, use programmed instructions or artificial intelligence and
                     then make decisions based on sensor input and logic.


                                 AI RoboGenius - VIII
                 32]]></page><page Index="35"><![CDATA[Characteristics of Robots

                 Robots have several important characteristics that set them apart:
                    Mechanical  structure: Robots  are made  up of physical  components,  such as actuators
                    (motors and servos), robotic arms, sensors and wheels, which allow them to move and interact
                    with their surroundings.

                    Programmability: Robots  can be programmed  with specific  instructions  to  perform tasks
                    automatically. This enables them to carry out actions exactly as directed.
                    Autonomy: Many robots can operate independently, completing tasks without the need for
                    constant human supervision.

                    Sensors and feedback: Robots are often equipped with sensors that help them understand
                    and  react  to changes  in their  environment,  allowing them  to  make  decisions  based  on
                    real-time data and feedback loops.
                    Artificial intelligence: Some robots use AI, which allows them to learn from data and adjust
                    their behaviour, much like humans do when encountering new experiences.

                 Advantages of Robots

                 Some advantages of robots are as follows:

                    High efficiency: Robots can work continuously without breaks, increasing productivity and
                    reducing production time.

                    Accuracy and precision: They perform tasks with a high level of accuracy, minimising errors
                    in manufacturing and assembly.

                    Safety: Robots can handle dangerous, repetitive or heavy tasks, keeping humans safe from
                    accidents and injuries.

                    Consistency: Unlike humans, robots  do not tire  or lose focus, so they  maintain the same
                    quality of work throughout.
                    Ability to work in hazardous environments: Robots can operate in extreme temperatures,
                    underwater or in space — places that may be unsafe for humans.

                    24/7 operation: Robots can work around the clock without the need for rest, holidays or shifts.

                 Limitations of Robots

                 While robots are incredibly useful, they also have some limitations:

                    Lack of creativity: Robots can only perform tasks they have been specifically programmed
                    for. They lack the ability to think creatively or come up with new ideas on their own.
                    Dependence on programming: Robots rely entirely on their programming. If there is an error
                    in the code or the instructions, the robot may not function correctly.

                    Limited problem-solving abilities: Unlike humans, robots struggle to adapt to unexpected
                    situations  or solve problems  that  fall  outside  their  defined  algorithms  or programmed
                    parameters.


                                                                  The World of Robots: Ethics, Laws and Future
                                                                                                                    33]]></page><page Index="36"><![CDATA[High cost: Building and maintaining robots can be expensive, especially for complex tasks or
                     advanced or industrial-grade models.

                     Vulnerability to malfunctions: Robots can break down due to mechanical failures, software
                     bugs or sensor malfunctions, which can disrupt their ability to perform tasks.
                     Job displacement: Robots can replace human workers in various industries, which may lead

                     to job loss and create economic challenges.
                     Ethical  concerns: The increasing  complexity of robots  raises  questions  about  their  role in

                     society, privacy issues and the ethics of robots making decisions that impact human lives.


                  GENERATIONS OF ROBOTS

                  Robots  have  evolved  through several generations,  each  marked  by  key  technological
                  advancements.  Below are the different generations  along with the approximate  years  when
                  they emerged:

                  1.  First  Generation - Mechanical  Robots (1950s - 1960s):
                     These robots were simple mechanical devices designed to
                     perform repetitive tasks, often in manufacturing. They were
                     manually  operated  and controlled  and  had  very limited
                     flexibility.  The most  notable  example  is  the  Unimate, the
                     first industrial robot, which introduced at General Motors in
                     1961 to handle die-casting and welding operations.






                                                     2.  Second Generation - Programmable Robots (1970s - 1980s):
                                                        Robots of this generation could be programmed to perform
                                                        specific  tasks,  offering more  flexibility  than  the  first
                                                        generation.  They  were used  in  industries  like  automotive
                                                        manufacturing  for more complex  tasks,  like  welding  and
                                                        painting. However, these robots still required human input
                                                        for programming and operation.





                  3.  Third Generation - Autonomous Robots (1990s - 2000s): Robots began

                     to operate with greater autonomy, using sensors and basic AI to make
                     decisions based on their environment. These robots could perform tasks
                     like inspection, material handling and packaging without human control.
                     The ASIMO robot, introduced by Honda in 2000, was one example of a
                     robot with basic autonomous capabilities such as walking, climbing stairs
                     and interacting with humans through voice and gesture recognition.



                                 AI RoboGenius - VIII
                 34]]></page><page Index="37"><![CDATA[4.  Fourth Generation - Intelligent Robots (2010s): Robots in
                                                       this generation are equipped with advanced AI computer
                                                       vision and machine learning, enabling them to learn from
                                                       experience and adapt to changes in their environment. They
                                                       can perform complex  tasks  such as assisting  in medical
                                                       surgeries, autonomous navigation and industrial precision
                                                       work. Self-driving cars and robotic surgery assistants are
                                                       examples of these intelligent robots.



                 5.  Fifth Generation - Advanced AI and Human-like Robots

                    (2020s  and  beyond): The latest  generation  focuses  on
                    robots that can interact with humans in more natural ways,
                    handle complex and dynamic environments. These robots
                    use advanced AI, machine learning and natural language
                    processing.  Notable  examples  include  Boston  Dynamics’
                    Atlas  robot, which can perform parkour and  Sophia  the

                    robot, known for engaging in conversations and expressing
                    human-like emotions.


                 ETHICS IN ROBOTS

                 With the rise of advanced robots and their increasing use in daily life, ethical concerns have
                 become more prominent. Some of the key issues include:

                    Robots in everyday life: Robots are becoming part of our daily routines, helping with tasks like
                    cleaning, cooking and even working in hospitals and schools. While they are mostly helpful,
                    their increasing presence raises important questions about how they should be used.
                    Roboethics: Roboethics focuses on the behaviour of robots and the ethical issues they bring.
                    With advancements in robotics and AI, it’s crucial to consider how these technologies affect
                    human values, decision-making and equality within society.
                    Ethical  concerns: As  robots  become  more advanced,  issues  such as  privacy,  fairness,
                    accountability and human control require careful attention. Some uses of robots, like armed
                    drones in the military or robotic body parts in bionics, raise concerns about safety and human
                    rights. These areas could lead to moral and legal dilemmas if not properly regulated.

                    Connected robotic systems: Robots can now connect to the internet and work together to
                    solve problems. In the future, robots may be as common as smartphones, but this raises
                    concerns about data privacy and security.
                    Learning robots: Robots with machine learning can adapt to people’s habits and remember
                    preferences, which raises privacy concerns and questions about control.
                    Legal challenges: Clear  legal frameworks and  ethical  standards  must  be  established  to
                    prevent potential misuse and to maintain public trust.



                                                                  The World of Robots: Ethics, Laws and Future
                                                                                                                    35]]></page><page Index="38"><![CDATA[Human responsibility: The creators, developers and users of robots hold the responsibility for
                     ensuring their safe and ethical use. Raising awareness and setting guidelines are essential to
                     make sure robots are developed and operated responsibly.

                     Future ethical questions: As robots get smarter, we must consider whether they should follow
                     ethical rules and how society will change with so many robots.


                  ROBOTICS

                  Robotics  is the branch  of technology  that  focuses on the design, creation  and operation  of
                  robots. A robot is a machine that can carry out tasks automatically or with minimal human
                  intervention,  often  by  following programmed  instructions  or using  sensors  and  artificial
                  intelligence to make decisions.


                  LAWS OF ROBOTICS

                  The  Three Laws of Robotics are a set  of rules created  to  guide  robots  to  behave safely and
                  ethically. These laws were first introduced by the science fiction author Isaac Asimov in his stories.
                  The laws are:
                  1.  A robot may not injure a human being or, through inaction, allow a human being to come to
                     harm.

                  2.  A robot must obey the orders given by human
                     beings,  except  where  such orders would                           If a robot saves one life but
                     conflict with the First Law.                                        endangers another, was its
                                                                                              decision ethical?
                  3.  A robot must protect its own existence as long
                     as such protection does not conflict with the
                     First or Second Law.


                  REAL-WORLD ROBOTS

                  Robots are no longer just machines from science-fiction films. They are real, useful and found
                  in almost every part of life today. From factories to hospitals and even outer space, robots are
                  helping humans in new and exciting ways.





                                                    Industry

                                                    Factories  use robots  to build  cars, assemble  products  and
                                                    lift  heavy  objects.  They  can  repeat  the  same  job  with  great
                                                    speed and accuracy, helping companies save time and reduce
                                                    mistakes.








                                 AI RoboGenius - VIII
                 36]]></page><page Index="39"><![CDATA[Healthcare

                 Robots now assist doctors and nurses in hospitals. They help
                 perform delicate  surgeries,  deliver medicines  and monitor
                 patients. Some even support people during recovery by guiding
                 exercises or helping them move safely.







                                                   Homes

                                                   Many  homes have robots  that  help with  daily  chores.
                                                   Vacuum-cleaning  robots,  robotic  mops  and  smart  home
                                                   assistants  like Amazon  Alexa or Google  Assistant can follow
                                                   voice commands to make life easier.




                 Defence

                 The military  uses  robots to  handle dangerous  tasks such as
                 bomb disposal, surveillance and rescue missions. These robots
                 keep  soldiers  safe by entering risky  areas where  humans
                 cannot go.


                                       India’s first robotic police officer,
                             SMART     named KP-Bot, was deployed in
                             SPARK
                                         Kerala Police Headquarters.





                                                   Space

                                                   Space  agencies  such as NASA  use  robots  called  rovers to
                                                   explore planets. For example, the Perseverance rover on Mars
                                                   collects rock samples and sends high-resolution images back to
                                                   Earth to help scientists learn about the planet.








                 Agriculture

                 Farmers now use robots to plant seeds, water crops and check
                 soil conditions. These robots help grow food faster and reduce
                 waste, making farming more efficient.


                                                                  The World of Robots: Ethics, Laws and Future
                                                                                                                    37]]></page><page Index="40"><![CDATA[VISUAL VAULT
                          Look at the picture and identify the areas where robots are used, naming each one.















                          1.                     2.                     3.                    4.




                  THE FUTURE OF ROBOTICS AND ROBOTS


                  The world of robotics is growing quickly. In the future, robots will become even smarter, faster
                  and more helpful. They will  work alongside  humans, not just  in factories,  but  also in schools,
                  hospitals, homes and even outer space.

                  Smarter and More Independent

                  Future robots will be able to think and make simple decisions on their own. With the help of
                  Artificial Intelligence (AI) and machine learning, they will learn from experience, just like humans

                  do. For example, a robot assistant could understand your daily routine and help you manage your
                  time better.

                  Working with Humans

                  Robots will not replace humans completely but will work collaboratively with them to make jobs
                  safer and easier. In construction, they could handle heavy or dangerous work, while humans focus
                  on planning and design. In hospitals, robots might support doctors during surgeries or take care

                  of patients who need continuous monitoring.

                  Everyday Life

                  At home, robots will become part of our daily lives. They might cook meals, clean rooms, water
                  plants or even help the elderly. Self-driving  cars and delivery  drones will  also become  more
                  common, making travel and transport faster and safer.

                  Exploring New Worlds

                  In space exploration, robots will continue to help scientists study planets, collect samples and

                  work in places where humans cannot survive. Underwater robots will explore deep oceans and
                  discover new marine species.





                                 AI RoboGenius - VIII
                 38]]></page><page Index="41"><![CDATA[Ask AIRO

                      How will robots change in the future with AI improvements and what new abilities will they have?



                 Challenges Ahead

                 Even though the future of robotics looks bright, there are challenges too. People will need to make
                 sure robots are used responsibly. Issues such as job loss, safety and data privacy will have to be
                 managed carefully.



                 ECOTECH      Robotic  drones help by  tracking  wildlife, planting  trees, monitoring  forests and
                 ENERGY       collecting data to protect the environment.








                    «   A robot is a machine made up of mechanical, electrical and sometimes electronic
                       parts, created to carry out specific tasks.


                    «   Robots have evolved from simple mechanical machines to intelligent systems that can think,
                       learn and interact with humans.

                    «   Robots  have evolved  through  several generations, each  marked  by  key  technological
                       advancements.

                    «   With the rise of advanced robots and their increasing use in daily life, ethical concerns have
                       become more prominent.
                    «   Robotics is the branch of technology that focuses on the design, creation and operation of robots.

                    «   The Three Laws of Robotics are a set of rules created to guide robots to behave safely and ethically.
                    «   The world of robotics is growing quickly. In the future, robots will become even smarter, faster and
                       more helpful.









                                                           Robo Check


                 A.  Tick ( ) the correct option.

                    1.   Identify  the  correct  generation  of robots  used  in the  automotive  industry  for welding
                        and painting.

                                (i)  First                                (ii)  Second

                               (iii)  Third                              (iv)  Fourth




                                                                  The World of Robots: Ethics, Laws and Future
                                                                                                                    39]]></page><page Index="42"><![CDATA[2.  Select the robot that is an example of a first-generation robot.
                                                                                                                                            COMPETENCY-BASED QUESTIONS
                                  (i)  Unimate                              (ii)  Atlas
                                                                                                                                            1.  A robot is assisting a surgeon during a complex medical procedure, performing highly precise
                                (iii)  ASIMO                              (iv)  Pepper                                                          movements. What generation of robot would most likely be used in this situation?

                     3.  Mark the kind of robots that help farmers in agriculture.                                                          2.  A construction robot lifts large and heavy objects in a building project. What characteristic of
                                                                                                                                                the robot allows it to handle such demanding tasks efficiently?
                                  (i)  Drones                               (ii)  Humanoids

                                (iii)  Vacuum cleaners                    (iv)  Military robots
                     4.  Mark the option that is not a limitation of robots.

                                  (i)  Lack of creativity                   (ii)  High cost


                                (iii)  Mechanical structure               (iv)  Job displacement
                     5.  Which generation of robots involves advanced AI and machine learning?

                                  (i)  First                                (ii)  Second

                                (iii)  Fourth                             (iv)  Fifth


                  B.  Write ‘T’ for true and ‘F’ for false.

                     1.  Robotics is the science of designing and building robots.


                     2.  A robot can only perform tasks that humans do not do.

                     3.   Roboethics focuses on the behaviour of robots and the ethical issues
                         they bring.


                     4.  Boston Dynamics’ Atlas robot is not example of fifth generation robot.

                     5.   Second generation robots did not require any human input for
                         programming or operation.


                  C.  Fill in the blanks using the given hints:
                     1.  Robots can be                   to repeat the same actions again and               Hints
                         again.                                                                             Human

                     2.  A robot can work autonomously without needing constant                          Robotic surgery
                                         control.                                                         Programmed
                     3.                  robots are used in industries like automotive                    Isaac Asimov
                                                                                                            Second
                         manufacturing for more complex tasks.                                             generation
                     4.   Self-driving cars and                assistants are an examples of fourth
                         generation robots.
                     5.  Laws of Robotics were first introduced by the science fiction author                    in his

                         stories.


                                 AI RoboGenius - VIII
                 40]]></page><page Index="43"><![CDATA[COMPETENCY-BASED QUESTIONS

                 1.  A robot is assisting a surgeon during a complex medical procedure, performing highly precise
                    movements. What generation of robot would most likely be used in this situation?
                 2.  A construction robot lifts large and heavy objects in a building project. What characteristic of

                    the robot allows it to handle such demanding tasks efficiently?




                                                            CASE STUDY




                    In an automotive plant, robots perform tasks like welding and assembly. They work autonomously,
                    using sensors and AI to  complete  tasks  with precision.  However,  robots  have limitations,  such
                    as lack of creativity  and high costs.  Robots  improve  speed  and efficiency  but  can’t  adapt  to
                    unexpected  situations.  Malfunctions  or errors in  programming  can  cause  production  delays.
                    Regular maintenance is expensive.


                   Based on the above case answer the following questions:
                  1.  What characteristics make robots valuable in manufacturing?



                  2.  What limitations of robots could affect production?


                  3.  How do ethical concerns impact robot use in industries?









                          People  who are skilled  in robotics  can pursue  careers as Robotics  Engineers,  Automation
                          Specialists, AI Ethics Analysts, Space Robotics Technicians or Bio-Robotics Researchers.
























                                                                  The World of Robots: Ethics, Laws and Future
                                                                                                                    41]]></page><page Index="44"><![CDATA[3
















                                                                                    Robot as a System






                  LEARNING OBJECTIVES


                     Building Blocks of Robots
                     Recognising and Understanding Robots
                     Robots Based on Size and Design





                           AIRO, I’ve seen robots in movies,
                           but how do they work?



                                                                   Robots have three main parts: mechanical,
                                                                                electronic and computational.






                           So, they have parts like our
                           body? What do they do?
                                                                    Yes, the mechanical part is like a skeleton,
                                                                   helping the robot move. The electronic part
                                                                    lets it sense things and the computational
                                                                       part is like the brain, making decisions.






                           Can robots think
                           for themselves?
                                                                       Not really. They follow instructions, but
                                                                      humans program and control them and
                                                                           that’s what makes robots amazing.







                                 AI RoboGenius - VIII
                 42]]></page><page Index="45"><![CDATA[Robots consist of several key components, including arms or manipulators sensors, actuators and
                 controllers. The controller acts as the main processing unit, enabling the communication between
                 these components. Its main role is to interpret the information received from one part of the robot
                 to another. The task the robot is designed to perform is the input, while the task it actually carries
                 out is the output. Sensors detect and send signals as information, helping the robot understand
                 its environment.


                 BUILDING BLOCKS OF ROBOTS

                 A robot is made up of three main parts or blocks: mechanical, electronic and computational.
                 These blocks work together to give a robot its abilities and functions.


                                                                 Robot



                                    Mechanical Block         Electronic Block      Computational Block



                          Body           Joints       Actuators              Software                Algorithms




                                                Sensors        Microcontrollers       Circuits

                                                        Building Blocks of Robots

                 Mechanical Block

                 The  mechanical block  includes  the physical  parts of the robot  such as the body,  joints  and
                 actuators. It allows the robot to move and interact with its surroundings. For example, motors
                 help a robot walk, turn or lift objects.

                 Different robots have different mechanical designs — humanoid robots have multiple joints to
                 move like  humans, drones have light frames to  fly  and underwater robots  have waterproof
                 sealed, pressure-resistant bodies with propellers for movement.
                 Electronic Block

                 The electronic block helps the robot sense, process and respond to its environment. It includes
                 sensors, microcontrollers and circuits. Sensors act as the robot’s eyes and ears, allowing it to
                 detect light, sound, distance or movement. The electronic block collects this data and sends it to
                 the controller for processing.

                 For instance, drones use cameras and GPS, underwater robots use sonar and mobile robots use
                 sensors to detect obstacles.

                 Computational Block

                 The computational block is like the robot’s brain. It includes software and algorithms that process
                 information, make decisions and control the robot’s actions.



                                                                                       Robot as a System
                                                                                                                    43]]></page><page Index="46"><![CDATA[This block helps robots think, plan and respond. For example, humanoid robots use algorithms to
                  balance and recognise speech, drones use them to control flight and mobile robots use them to
                  plan routes and avoid obstacles.


                  RECOGNISING AND UNDERSTANDING ROBOTS

                  Robots can be identified by observing how they look, move and perform tasks. This can be done
                  through demonstrations, videos or by studying their technical details.

                     Observing robots in action: Watching a robot perform tasks can give you important insights
                     into its design and capabilities. For instance, observing a robot move, pick up objects or interact
                     with its environment can help you understand its mobility, precision and level of automation.
                     Exploring robots through videos: Videos can provide a closer look at what robots can do.
                     They can show robots performing complex tasks like walking, flying or solving problems and
                     give a visual understanding of their design and control systems.

                     Understanding technical details: Examining a robot’s technical specifications, such as its
                     size, weight, power needs and parts like actuators and sensors, can reveal how the robot
                     works. The robot’s control system and software also indicate its intelligence and decision-
                     making skills.
                     Considering the robot’s purpose: The intended purpose of a robot is key to understanding its
                     capabilities. Some robots are designed for specific tasks like cleaning or manufacturing, while
                     others, such as humanoid robots, can do a variety of activities. Knowing its purpose helps
                     identify what it can do and where it may have limitations.


                  ROBOTS BASED ON SIZE AND DESIGN


                  Robots come in many sizes and shapes, depending
                  on what they are made to do. The size and design            SMART        Japan has 40% of the
                  of a robot depend on the task it is built for. Let’s look    SPARK    world’s robots, making it the
                  at  different  types  of robots,  from the  smallest  to                leader in robot numbers.
                  the largest.
                  Microbots: Tiny Robots

                  Microbots are tiny robots, usually less than a millimetre in size. Despite their small size, they are
                  incredibly powerful and can perform jobs that require a high level of precision. These robots are
                  designed to work in very small spaces, which makes them perfect for tasks like working inside the
                  human body or in environments that are difficult for larger robots to reach.

                  Some examples of microbots are:
                  1.  Swarm  Robots: These  are  groups  of  small robots  that  work
                     together  to  complete  tasks.  For example,  they  can be  used
                     to  monitor  environmental  conditions,  such as  measuring  air
                     quality  or even respond  to  emergencies,  such as helping in
                     rescue operations.



                                 AI RoboGenius - VIII
                 44]]></page><page Index="47"><![CDATA[2.  Bio-Inspired Microbots: These  microbots  are  designed  to
                                                       mimic the movements of living creatures. In medicine, they
                                                       can be used to deliver drugs directly to a specific part of the
                                                       body or even assist in surgeries. This makes them incredibly
                                                       useful for tasks that require great accuracy and care.




                 Some advantages of microbots are:
                    Precision: They are designed for accurate tasks.

                    Versatility: They can be used in medicine, environmental monitoring and more.

                    Cost-Effective: Their small size makes them less expensive to manufacture.

                 Insectbots: Small Robots Inspired by Insects

                 Insectbots are small robots designed to move and act like insects. They are lightweight and can
                 easily  navigate  through tight  spaces,  making  them very useful for  tasks  where  larger  robots
                 would struggle. These robots are inspired by the way insects move, whether by crawling, flying or
                 climbing.

                 Some examples of insectbots are:
                 1.  RoboFly: This robot is designed to mimic the movement of a
                    fly. It can fly freely, just like a real fly and is capable of flying

                    through tight spaces or around obstacles.





                                                  2.  DelFly: DelFly  is  inspired  by  a dragonfly and can navigate
                                                      complex environments with ease. It can fly, hover, and glide,
                                                      making  it  useful  for exploring  difficult-to-reach  areas  and
                                                      conducting aerial surveillance or inspection tasks.




                 3.  Harvard RoboBee: The RoboBee is a tiny flying robot inspired
                    by bees. It is used in tasks like artificial pollination, which is
                    important  for  agriculture. Its small size  and flight abilities
                    make it ideal for performing tasks that require high precision
                    and control.

                 Some advantages of insectbots are:

                    Adaptability: They can crawl, fly or climb, making them suitable for many environments.
                    Energy efficient: They use less power than larger robots.
                    Small size: Their small size allows them to work in places larger robots can’t reach.



                                                                                       Robot as a System
                                                                                                                    45]]></page><page Index="48"><![CDATA[Snakebots: Flexible Robots

                  Snakebots  are robots  that  are designed  to  move  like  snakes.  They are made  up  of multiple
                  segments that can bend and twist, giving them incredible flexibility. This unique design allows
                  snakebots to fit into narrow spaces and perform tasks that require precision.
                  Some examples of snakebots are:


                                                    1.  Modular Snakebots: These snake-like robots are made of
                                                        separate and interchangeable parts. This allows them to be
                                                        rearranged and customised for different tasks. For example,
                                                        they  can be adjusted  to  fit the specific  requirements  of a
                                                        mission, whether it’s exploring confined spaces or performing
                                                        delicate operations.



                  2.  Soft Snakebots: Soft  snakebots  are made  from flexible
                     materials,  that  allow them  to  bend  and  move  smoothly
                     through complicated environments. These robots can slither
                     through confined areas, such as pipes, tunnels, or cracks in
                     structures, where most other robots cannot reach.

                  Some advantages of snakebots are:
                     Flexibility: They can move in many directions, making them ideal for narrow spaces.

                     Precision: They can perform tasks like surgery with great care.
                     Adaptability: Can be adjusted for various tasks.


                        Ask AIRO

                       Propose a hybrid robotic system combining characteristics of snakebots and insectbots for search
                       and rescue operations.

                  Industrial Robots: Large Robots for Factories

                  Industrial  robots  are  large, programmable  machines  designed  to  perform  specific  tasks  in
                  factories,  such as assembly,  welding and packaging.  These robots  are built  to  be  fast and

                  precise, making them perfect for work in industries where high speed, consistency and accuracy
                  are critical.

                  Some examples of industrial robots are:

                                           1.  KUKA Robots: KUKA robots are used in automotive manufacturing
                                               plants.  They  are  primarily  responsible  for assembling  vehicle

                                               components and performing automated welding operations. These
                                               robots are incredibly precise, ensuring that car parts are put together
                                               quickly and correctly.



                                 AI RoboGenius - VIII
                 46]]></page><page Index="49"><![CDATA[2.  ABB  Robots: ABB  robots  are widely  used  in factories  for

                    various tasks like packaging, sorting and testing products.
                    These robots  help streamline production  by  carrying out
                    repetitive tasks that would be time-consuming for humans.

                 Some advantages of industrial robots are:

                    Efficiency: They perform tasks faster and more accurately than humans.
                    Safety: They handle dangerous jobs, keeping workers reducing the risk of workplace injuries.

                    Durability: They can work around the clock without breaks, increasing production.

                 Humanoid Robots: Robots That Look Like Humans

                 Humanoid robots are robots designed to resemble, move and behave like humans. They are built
                 with multiple joints and actuators that allow them to imitate human actions, such as walking,
                 talking and even showing emotions. These robots are capable of interacting with people, making
                 them ideal for various social and practical tasks.

                 Some examples of humanoid robots are:


                                                   1.  ASIMO: ASIMO is a humanoid robot created by Honda. It can
                                                       walk, talk and interact with people. ASIMO was designed to
                                                       assist with tasks such as helping the elderly or serving drinks,
                                                       demonstrating its ability to perform human-like actions.










                 2.  Sophia: Sophia is a well-known humanoid robot developed by
                    Hanson Robotics. She is designed to interact with humans and
                    can display  a range of emotions,  from happiness to  sadness.
                    Sophia has gained attention for her ability to hold conversations

                    and engage in human-like interactions.
                 Some advantages of humanoid robots are:

                    Human Interaction: They are great for customer service, teaching and healthcare.

                    Versatility: They can perform tasks like serving drinks or greeting guests.


                                              A child with no siblings spends all their time with a robot
                                           programmed to be their best friend. Is it healthy for children to
                                            form deep emotional bonds with machines instead of people?





                                                                                       Robot as a System
                                                                                                                    47]]></page><page Index="50"><![CDATA[4.  Which block includes components like sensors and microcontrollers?
                   ECOTECH     Tiny RoboBees help pollinate crops. They support farmers when real bee numbers are low.
                   ENERGY                                                                                                                                   (i)  Mechanical Block                     (ii)  Electronic Block


                                                                                                                                                          (iii)  Computational Block                 (iv)  Structural Block

                                                                                                                                                5.  Mark the category to which Swarm Robots belong.


                                                                                                                                                            (i)  Industrial Robots                    (ii)  Microbots
                     «   A robot is made  up of three  main  parts  or blocks:  mechanical,  electronic  and
                         computational.                                                                                                                   (iii)  Humanoid Robots                     (iv)  Snakebots

                     «   The mechanical  block  includes  the  physical  parts  of the  robot  such  as  the  body,  joints  and
                         actuators.                                                                                                         B.  Write ‘T’ for true and ‘F’ for false.

                     «   The electronic block helps the robot sense and understand its environment.                                             1.  Insectbots are typically very large and heavy.

                     «   The computational block acts as the robot's brain, processing information, making decisions and                        2.  Soft snakebots are made from flexible materials.
                         controlling its actions through software and algorithms.

                     «   Robots can be identified by observing how they look, move and perform tasks.                                           3.  All robots are large and used in factories.

                     «   Robots come in many sizes and shapes, depending on what they are made to do.                                           4.  Industrial robots like those from ABB can perform packaging tasks.
                     «   Industrial  robots  are  large, programmable  machines  designed  to  perform  specific  tasks  in                     5.  Humanoid robots like Sophia can display emotions.
                         factories, such as assembly, welding and packaging.

                     «   Humanoid robots are robots designed to resemble, move and behave like humans.                                      C.  Fill in the blanks using the given hints:

                                                                                                                                                1.  The                 block allows the robot to move and interact with its
                                                                                                                                                   surroundings.                                                                       Hints
                                                                                                                                                                                                                                        Car
                                                                                                                                                2.                  are tiny robots less than a millimeter in size.                   Sensors
                                                                                                                                                                                                                                        ABB
                                                            Robo Check                                                                          3.  KUKA robots are used in                   manufacturing plants.                  Mechanical


                  A.  Tick ( ) the correct option.                                                                                              4.                  robots help streamline production by carrying out                Microbots
                                                                                                                                                   repetitive tasks.
                     1.   Select the tiny robot inspired by bees and used for tasks like pollination.
                                                                                                                                                5.                  act as the robot’s eyes and ears allowing it to detect light,
                                  (i)  RoboFly                              (ii)  DelFly
                                                                                                                                                   sound, distance or movement.
                                (iii)  Harvard RoboBee                    (iv)  Sophia
                                                                                                                                            COMPETENCY-BASED QUESTIONS
                     2.  Identify the part of a robot described as its brain.
                                                                                                                                            1.  A factory uses a large robotic arm to weld car parts with extreme accuracy. Based on the
                                  (i)  Actuators                            (ii)  Mechanical Block
                                                                                                                                                three building blocks, which block is responsible for the physical movement of the welding arm
                                (iii)  Electronic Block                   (iv)  Computational Block                                             and what specific components would it include?


                     3.  Pick the robot that is an example of a humanoid robot.                                                             2.  You are at a tech exhibition and see a robot that can change its shape by rearranging its
                                                                                                                                                segments to either crawl through a pipe or climb a small wall. What type of robot is this and
                                  (i)  KUKA Robot                           (ii)  ABB Robot                                                     what specific category does it fall under?

                                (iii)  ASIMO                              (iv)  DelFly



                                 AI RoboGenius - VIII
                 48]]></page><page Index="51"><![CDATA[4.  Which block includes components like sensors and microcontrollers?
 ECOTECH   Tiny RoboBees help pollinate crops. They support farmers when real bee numbers are low.
 ENERGY                         (i)  Mechanical Block                     (ii)  Electronic Block


                               (iii)  Computational Block                (iv)  Structural Block

                    5.  Mark the category to which Swarm Robots belong.


                                (i)  Industrial Robots                    (ii)  Microbots

                               (iii)  Humanoid Robots                    (iv)  Snakebots

                 B.  Write ‘T’ for true and ‘F’ for false.


                    1.  Insectbots are typically very large and heavy.

                    2.  Soft snakebots are made from flexible materials.

                    3.  All robots are large and used in factories.


                    4.  Industrial robots like those from ABB can perform packaging tasks.

                    5.  Humanoid robots like Sophia can display emotions.


                 C.  Fill in the blanks using the given hints:

                    1.  The                 block allows the robot to move and interact with its
                        surroundings.                                                                      Hints
                                                                                                            Car
                    2.                  are tiny robots less than a millimeter in size.                    Sensors
                                                                                                            ABB
                    3.  KUKA robots are used in                   manufacturing plants.                  Mechanical

                    4.                  robots help streamline production by carrying out                 Microbots

                        repetitive tasks.
                    5.                  act as the robot’s eyes and ears allowing it to detect light,
                        sound, distance or movement.



                 COMPETENCY-BASED QUESTIONS

                 1.  A factory uses a large robotic arm to weld car parts with extreme accuracy. Based on the
                    three building blocks, which block is responsible for the physical movement of the welding arm
                    and what specific components would it include?

                 2.  You are at a tech exhibition and see a robot that can change its shape by rearranging its
                    segments to either crawl through a pipe or climb a small wall. What type of robot is this and
                    what specific category does it fall under?






                                                                                       Robot as a System
                                                                                                                    49]]></page><page Index="52"><![CDATA[CASE STUDY




                     A manufacturing company has introduced advanced robots to automate its assembly line. The
                     robots  are designed  with three main  blocks:  mechanical,  electronic  and computational.  The
                     mechanical block includes components like motors and actuators, allowing the robots to move
                     and interact with objects. The electronic block consists of sensors and microcontrollers, helping
                     the robots to detect their environment, such as the presence of objects and their distance. The
                     computational  block,  made  up  of algorithms  and  software,  controls  the  robots'  actions  and
                     decision-making. While the robots have significantly improved production speed and precision,
                     In an automotive plant, robots perform tasks like welding and assembly. They work autonomously,
                     the company has started to notice limitations. The robots are unable to adapt quickly to tasks
                     using sensors and AI to  complete  tasks  with precision.  However,  robots  have limitations,  such
                     outside their programming and are not effective in handling changes in production requirements.
                     as lack of creativity  and high costs.  Robots  improve  speed  and efficiency  but  can’t  adapt  to
                     The factory has to halt production whenever there is a change in the assembly process, requiring
                     unexpected  situations.  Malfunctions  or errors in  programming  can  cause  production  delays.
                     the robots to be reprogrammed. This issue results in downtime and loss of efficiency.
                     Regular maintenance is expensive.

                     Based on the above case answer the following questions:

                    1.   How do  the  mechanical,  electronic  and  computational  blocks  of robots  contribute  to  their
                       functionality in an assembly line?


                    2.  What challenges might the company face due to the robots' limited flexibility?


                    3.  How can the company improve the robots’ ability to adapt to new tasks?










                            People  who are skilled  in robotics  can pursue  careers in emerging  fields  such as Swarm
                            Robotics, Biomedical Engineering, Human–Robot Interaction and Soft Robotics.
























                                 AI RoboGenius - VIII
                 50]]></page><page Index="53"><![CDATA[4







                                                                                             Introduction


                                                                                                     to Google



                                                                                 Teachable Machine






                 LEARNING OBJECTIVES


                    Google Teachable Machine
                   Working of Google Teachable Machine
                   Creating a Project using Google Teachable Machine





                          AIRO, how does a machine learn?
                          I thought it needed a teacher.               That’s a good question! Machines can

                                                                       learn from examples such as pictures,
                                                                        sounds or poses by using a tool, such
                                                                               as Google Teachable Machine.






                          What does Google
                          Teachable Machine do?

                                                               It helps you teach a computer without coding.
                                                                   You just upload examples and it learns to
                                                                                              recognise them.






                          Sounds great!
                          How do I start?
                                                                        Go to the Google Teachable Machine
                                                                         website, choose a project and begin
                                                                                     exploring how AI learns!







                                                                    Introduction to Google Teachable Machine
                                                                                                                    51]]></page><page Index="54"><![CDATA[Have you ever wondered how your phone can recognise your face to unlock itself or how some
                  apps  can identify  songs  just  by  listening?  This amazing  ability  is  made  possible  by  machine
                  learning, where computers learn from examples, just like humans do. In simple terms, machine
                  learning is a type of artificial intelligence (AI) that allows computers to learn and improve from
                  experience without being explicitly programmed.
                  In this chapter, we'll explore Google Teachable Machine, a free, fun and easy-to-use online tool
                  that lets you create your own machine learning models. You don’t need to be an expert or a
                  computer scientist. With just a few clicks, you can teach a computer to recognise different objects,
                  sounds or even body poses.



                   GOOGLE TEACHABLE MACHINE

                  Google Teachable Machine is a web-based tool created by Google that allows users to build
                  machine learning models without needing any coding experience. It’s designed to help people,
                  especially  students,  understand  how artificial  intelligence  (AI) works by  offering a  hands-
                  on, interactive learning experience. With Google Teachable Machine, you can train models to
                  recognise images, sounds and even poses using your webcam or microphone.
                  By uploading data like photos or sounds and sorting them into categories, users can create custom
                  AI models that make predictions. It’s a fun and easy way for students to explore the basics of
                  machine learning and AI.

                  Key Features of Google Teachable Machine

                  Some key features of Google Teachable Machine are as follows:

                     Easy to use: It has a simple drag-and-drop interface. If you can use a website, you can use
                     Google Teachable Machine.
                     No coding required: You can build powerful AI models without writing a single line of code.

                     Versatile: You can create projects that recognise images (such as objects or facial expressions),
                     sounds (such as claps or snaps) and poses (such as hand gestures or dance moves).
                     Fast training: It trains your model quickly in your web browser, so you can see the results of
                     your work in minutes.

                     Free and accessible: It is completely          SMART       Google Teachable Machine enables
                     free to use and works on any computer          SPARK     sign-language recognition that translate
                     with an internet connection.                                  gestures into text or speech.



                  WORKING OF GOOGLE TEACHABLE MACHINE

                  Google Teachable Machine works in a few simple steps.

                    STEP - 1   Collecting data: The first step is to gather data such as images, sounds or poses. You
                               then organise this data into different categories or classes. For example, if you are
                               training a model to recognise fruits, you might have separate categories for apples,
                               bananas and oranges.


                                 AI RoboGenius - VIII
                 52]]></page><page Index="55"><![CDATA[STEP - 2   Training  the  model:  Once the  data  is  ready,  the  tool  uses  it  to  train a  machine
                              learning model. During training, the model learns to recognise patterns and features
                              in the data so it can predict the correct category when it sees new input.

                  STEP - 3   Testing the model: After training, you can test your model in real time. This allows
                              you to see how accurately it recognises new images, sounds or poses. You can also
                              improve the model’s accuracy by adding more data or adjusting categories.


                  STEP - 4   Using the model: Once you are satisfied with the model’s performance, it can be
                              exported and integrated into websites, apps or other digital projects. This allows you
                              to apply your AI model in real-world applications such as games, educational tools
                              or assistive technologies.


                      Ask AIRO

                      Create a Rock, Paper, Scissors Model using Google Teachable Machine.



                 How to Improve Your Model?

                 To make  your model  smarter and more  accurate,  follow these  steps  to  refine and enhance
                 its overall performance:
                    Add more data: The more examples you provide, the better your model will learn. Try to
                    collect a variety of data covering different scenarios, so the model can recognise patterns
                    more accurately.

                    Use clear and varied data: Make sure your data is clean, clear and high in quality. Include
                    different examples  so your model  can handle different situations,  like  changes in lighting
                    or angles.
                    Test the model: Regularly test how well your model performs with new data. This helps you see
                    what needs improvement and where it is doing well.

                    Adjust  settings: You can adjust  the settings in Google  Teachable Machine, like  training
                    time  and  learning parameters.  Fine-tuning  these  settings  can help the  model  learn
                    more effectively.
                    Retrain the model: After adding data or adjusting the settings, retrain the model. This helps it
                    learn from the new information and improve its predictions.




                 CREATING A PROJECT USING GOOGLE TEACHABLE MACHINE

                 To create a new project in Google Teachable Machine, follow the given steps:

                  STEP - 1   Open the Web browser.

                  STEP - 2   Type the URL https://teachablemachine.withgoogle.com in the address
                              bar and press the Enter key or scan the QR code.



                                                                    Introduction to Google Teachable Machine
                                                                                                                    53]]></page><page Index="56"><![CDATA[STEP - 3   Click on the Get Started button.




































                               A New Project window will appear.  You  will see  three  types  of projects:  Image
                               Project, Audio Project and Pose Project.

                   STEP - 4   Choose the type of project you want to create (for example, Image Project).






































                               A New Image Project dialog box will appear.



                                 AI RoboGenius - VIII
                 54]]></page><page Index="57"><![CDATA[STEP - 5   Click on the Standard image model option.


































                              The final window of Image Project will open, where you can add classes and train
                              your model.






























                 Create Classes and Add Training Data

                 A class is a category or a group you want your model to recognise. The default project has two
                 classes: Class 1 and Class 2.
                 To rename and add classes in Google Teachable Machine, follow the given steps:


                  STEP - 1   Click on the Class 1 and rename it to what you want to recognise, for example,
                              Apple.



                                                                    Introduction to Google Teachable Machine
                                                                                                                    55]]></page><page Index="58"><![CDATA[STEP - 2   Click on the Class 2 and rename it to what you want to recognise, for example,
                               Banana.

                   STEP - 3   To add another class, click on the Add a Class button.

































                   STEP - 4   Click on the Class 3 and rename it to what you want to recognise, for example,
                               Orange.

                   STEP - 5   Click on the Upload button inside the Apple class.









































                                 AI RoboGenius - VIII
                 56]]></page><page Index="59"><![CDATA[STEP - 6   Click on the Choose Images from your files or drag & drop here option.


































                              An Open window will open.

                  STEP - 7   Navigate to the folder on your computer where you saved your apple pictures.

                  STEP - 8   Select 20-30 clear photos of apples from your computer.

                  STEP - 9   Click on the Open button.








































                                                                    Introduction to Google Teachable Machine
                                                                                                                    57]]></page><page Index="60"><![CDATA[You will see images appear as small thumbnails.



























                  Similarly,  upload images for  the  Banana and  Orange classes in the same  way as the
                  Apple class.












































                  Train the Model

                  Training the model is the process of teaching the model to recognise and classify the images
                  you’ve uploaded.




                                 AI RoboGenius - VIII
                 58]]></page><page Index="61"><![CDATA[To train the model in Google Teachable Machine, follow the given steps:

                  STEP - 1   Click on the Train Model button in the Training section, wait for 1-3 minutes for the
                              training to complete and make sure not to close the window during the process.








































                  STEP - 2   Click on the down arrow button in the Preview section.

                              A drop-down list appears.

                  STEP - 3   Click on the File option in the drop-down list.

































                                                                    Introduction to Google Teachable Machine
                                                                                                                    59]]></page><page Index="62"><![CDATA[STEP - 4   Click on the Choose Images from your files or drag & drop here option.

                               An Open window will open.

                   STEP - 5   Navigate to the folder on your computer where you saved your pictures.

                   STEP - 6   Select a fruit photo to test your model.

                               Check the confidence bars for predictions - high percentages (above 80%) indicate
                               your model is accurately trained.
























                               OR

                               To capture images using your webcam, follow these steps:
                    STEP - 1   Click on the Webcam option in the drop-down list to activate your device camera.


                   STEP - 2   Hold up the relevant fruit and click Hold to Record to capture multiple images.

                   STEP - 3   Check the Preview to ensure the images look good before saving them.

                   STEP - 4   Move the fruit around to show different angles and lighting.
                               Check the confidence bars for predictions - high percentages (above 80%) indicate
                               your model is accurately trained.



























                                 AI RoboGenius - VIII
                 60]]></page><page Index="63"><![CDATA[VISUAL VAULT
                        Create three classes--Cricket Bat, Soccer Balls and Badminton Kit. Collect images for each,
                        upload them to Google Teachable Machine and train the model to classify them.

                        1.                             2.                                3.













                                                       CHALLENGE CHAIN

                  1.  Why does a model give more accurate results when it is trained with diverse data?



                  2.  What challenges arise when training a model to recognise multiple classes and how can
                      you address them?







                 Exporting the Model

                 Exporting makes the trained model available for use in other projects, either by uploading it for
                 sharing or downloading it to a device for real-time predictions. To export your trained model,
                 follow the given steps:

                  STEP - 1   Click on the Export button located at the top-right corner of the Preview section.






























                                                                    Introduction to Google Teachable Machine
                                                                                                                    61]]></page><page Index="64"><![CDATA[STEP - 2   Once you click on the Export button, you will see several options for exporting:

                                   Upload (shareable link): This option generates a shareable link to your model
                                   hosted by Google Teachable Machine.


                                   Download: This option allows you to download the model to your device.


















































                  Once exported, you can use the model in your projects for real-time predictions.



                                                 If you use photos or voices of others for training your
                                                model, you must first get their permission. This ensures
                                                             ethical and legal use of data.





                   ECOTECH     Google Teachable Machine can help raise climate change awareness by creating projects
                   ENERGY      that recognise weather patterns or environmental changes. These models can show how
                               the climate is shifting.







                                 AI RoboGenius - VIII
                 62]]></page><page Index="65"><![CDATA[STEP - 2   Once you click on the Export button, you will see several options for exporting:

                    «   Google Teachable Machine is a web-based tool created by Google that allows
      Upload (shareable link): This option generates a shareable link to your model   users to build machine learning models without needing any coding experience.
 hosted by Google Teachable Machine.
                    «   Google Teachable Machine allows you to build a powerful AI models without writing a single line
      Download: This option allows you to download the model to your device.  of code.

                    «   Google Teachable Machine collects data, trains a model, tests it and then exports it for use in
                       projects or apps.

                    «   A class is a category or a group you want your model to recognise.

                    «   Training the model is the process of teaching the model to recognise and classify the images
                       you've uploaded.

                    «   Exporting makes the trained model available for use in other projects, either by uploading it for
                       sharing or downloading it to a device for real-time predictions.










                                                           Robo Check



                 A.  Tick ( ) the correct option.

                    1.   Select the device used for training a model to recognise sounds.

                                (i)  Microphone                           (ii)  Webcam

                               (iii)  Speaker                            (iv)  Keyboard

                    2.  After collecting data and training the model, what should you do next?

                                (i)  Export the model                     (ii)  Test the model
 Once exported, you can use the model in your projects for real-time predictions.
                               (iii)  Add new data                       (iv)  Ignore the results


 If you use photos or voices of others for training your      3.  Identify the item that is not required to use Google Teachable Machine.
 model, you must first get their permission. This ensures
 ethical and legal use of data.     (i)  Internet connection              (ii)  Webcam

                               (iii)  Coding skills                      (iv)  Microphone


 ECOTECH   Google Teachable Machine can help raise climate change awareness by creating projects      4.  Which of the following type of data you need to collect for Google Teachable Machine?
 ENERGY  that recognise weather patterns or environmental changes. These models can show how      (i)  Only images      (ii)  Images, sounds and poses
 the climate is shifting.

                               (iii)  Only sounds                        (iv)  Only text




                                                                    Introduction to Google Teachable Machine
                                                                                                                    63]]></page><page Index="66"><![CDATA[5.  In the default project of Google Teachable Machine, how many classes are there?

                                  (i)  One                                  (ii)  Four

                                (iii)  Three                              (iv)  Two


                  B.  Write ‘T’ for true and ‘F’ for false.

                     1.  Google Teachable Machine is a web-based tool created by Microsoft.

                     2.   Google Teachable Machine offers a drag-and-drop interface for creating machine
                         learning models.


                     3.  You cannot upload images for training in Google Teachable Machine.

                     4.  You cannot test a model in real time after training in Google Teachable Machine.


                     5.  You can export a trained model to generate a shareable link or download it to
                         your device.


                  C.  Fill in the blanks using the given hints:
                     1.  The more examples you provide, the better your model will                    .


                     2.  Once the data is ready, the tool uses it to train a                  learning      Hints
                         model.                                                                             Retrain
                                                                                                            Machine
                     3.  After adding data or adjusting the settings,                    the model to
                                                                                                             Class
                         help it learn from the new information.
                                                                                                        Recognise, Classify
                     4.  A                 is a category or a group you want your model to                   Learn

                         recognise.

                     5.  Training the model is the process of teaching the model to
                         and                  the images you’ve uploaded.



                  COMPETENCY-BASED QUESTIONS

                  1.  Anjali has created a model to recognise different types of flowers based on images. After testing
                     it, she finds that the accuracy is lower than expected and some flowers are misclassified. What
                     can Anjali do to improve the model’s accuracy?

                  2.  Rohit has created a model that can accurately recognise different types of animals in wildlife
                     photos. After testing the model in various conditions, he is pleased with its performance and

                     now wants to use it in a conservation project to track endangered species in remote areas.
                     What should Rohit do next?





                                 AI RoboGenius - VIII
                 64]]></page><page Index="67"><![CDATA[Develop a model  to  classify different animals  based  on photos.  You  will  need  to  gather a variety  of
                images of animals from different categories (e.g., mammals, birds, reptiles) to train the model. Use photos
                with different angles, lighting and backgrounds to ensure accuracy. Test its performance using images
                from the  school’s  nature  collection,  ensuring  that  the  model  can  correctly  identify  animals  in  various
                real-world scenarios.







                Goal



                Setup






                Observation



















                          People  expert  in Google  Teachable  Machine  can pursue  their  careers as an AI and
                          Machine Learning Engineer, Data Scientist or Robotics Engineer.





























                                                                    Introduction to Google Teachable Machine
                                                                                                                    65]]></page><page Index="68"><![CDATA[maker mission


                                             Colour Detector in Google Teachable Machine

                  In this project, you’ll create a colour detection model using Google Teachable Machine. The goal is to teach
                  your machine to recognise and classify different colours using a camera. This model can be used in various
                  applications like recognising objects based on their colour, sorting items or even helping robots identify the
                  colour of things in their environment.

                  To create a colour detection model using Google Teachable Machine follow
                  the given steps:

                   STEP - 1    Select  the  Image  Project  option  on Google Teachable
                               Machine.






                                                STEP - 2   Gather images of colours you want to teach the model. Use
                                                            a variety of shades for accuracy. Ensure good lighting when
                                                            capturing each colour image.






                   STEP - 3   Upload images to Google Teachable Machine and label them
                               based on their respective colours.






                                                 STEP - 4   Train your model. Google Teachable Machine will process the
                                                             images and learn to recognise the differences between the
                                                             colours based on the data you’ve provided.




                   STEP - 5   Test your model with live input. Hold up coloured objects in
                               front of the  camera  and  check  if the  model  can  correctly
                               identify the colour.





                                                STEP - 6    If the model works as expected, export the trained model for
                                                            use in different applications, such as on a robot or a colour
                                                            sorting system.








                                 AI RoboGenius - VIII
                 66]]></page><page Index="69"><![CDATA[5



                                                                                                       Building


                                                                                                      Projects


                                                                                               with Google



                                                                                 Teachable Machine






                 LEARNING OBJECTIVES


                    Google Teachable Machine Projects
                    Image Projects
                    Audio Projects
                    Pose Projects



                         AIRO, I made my first image model. What
                         else can Google Teachable Machine do?




                                                           Well done, AVINA! It can also understand sounds
                                                                and body movements. Let’s check those out!





                          Sounds? Like hearing
                          different voices?



                                                                     Exactly! It can hear claps, snaps or even
                                                                          words and follow your movements.






                          That’s awesome! Can we
                          use these models in games?


                                                                              Yes, we can! Let’s learn how to
                                                                                    use them in real projects.







                                                               Building Projects with Google Teachable Machine
                                                                                                                    67]]></page><page Index="70"><![CDATA[In this chapter, you’ll learn how to create cool projects that use images, sound and body poses.
                  You’ll train your model to recognise different sounds, like claps or words and teach it to detect
                  movements or gestures with your body.


                   GOOGLE TEACHABLE MACHINE PROJECTS

                  Google Teachable Machine project is like teaching your computer to recognise patterns. You give
                  it with examples—like images, sounds or body poses and it learns to identify them. Once trained,
                  the computer can use this learning to make predictions or classifications when it encounters
                  new input.

                  In Google Teachable Machine, you can create three types of projects:
                  1.  Image Project

                  2.  Audio Project
                  3.  Pose Project

                  Using Google Teachable Machine in Real Projects

                  Google Teachable Machine can be used to create real-world applications, allowing individuals
                  to explore and apply machine learning concepts. Below are some ways to use Google Teachable
                  Machine in various projects.

                  Interactive Games
                  Google  Teachable  Machine  allows the  creation  of games  that  respond  to  real-time  physical
                  actions,  sounds  or images. It enables  the design of interactive  games  that  react  to  gestures,
                  movements or voice commands, enhancing user engagement and interactivity.

                  Smart Devices

                  With Google Teachable Machine, trained model can be integrated into smart devices. It can be
                  used  to  create  systems  that  respond  to  voice  commands  or gestures,  providing  control  over
                  various devices like lights, speakers or even home appliances.
                  Virtual Assistants

                  Google Teachable Machine helps in creating AI-powered virtual assistants that can recognise
                  voices, images or gestures. This assistant can be used to develop applications that interact in a
                  natural, human-like way, responding to commands or actions.
                  Object and Image Recognition

                  Google Teachable Machine is ideal for projects requiring object detection or image classification.
                  This capability allows for the development of systems that can automatically identify objects,
                  making it useful for tasks such as sorting, classification or automation.

                  Health and Fitness
                  Google Teachable Machine can be applied in health-related projects, such as tracking physical
                  activity or monitoring posture. It enables the creation of systems that can recognise movements
                  or gestures, providing feedback and assistance in fitness routines.



                                 AI RoboGenius - VIII
                 68]]></page><page Index="71"><![CDATA[Educational Tools

                 Using Google  Teachable  Machine, interactive  educational  tools  can be created  that  interact
                 with users. These tools can respond to answers, gestures or spoken words, providing a dynamic
                 learning experience and supporting various teaching methods.
                 Augmented Reality (AR)

                 Google Teachable Machine can be combined with augmented reality to create immersive real-
                 time interactive experiences. By integrating trained models with AR, interactive applications can
                 be developed that respond to physical objects or movements in real-time.
                 Creative Projects

                 Google Teachable Machine also supports creative projects by enabling integration of machine
                 learning with art, music or design. It can be used to create interactive installations or digital
                 experiences that respond to gestures or objects.


                 IMAGE PROJECTS

                 Image projects help a computer recognise and classify images into different groups. You can
                 provide images to the model either by uploading them or by capturing them through a webcam.
                 The model can learn to tell the difference between objects like apples, bananas, cats and dogs.
                 These projects are useful for tasks like sorting, recognising objects, identifying animals or even
                 detecting emotions.

                 Recognise Objects in the Classroom

                 To recognise objects in the classroom, follow the given steps:

                  STEP - 1   Open the Web browser.

                  STEP - 2   Type the URL https://teachablemachine.withgoogle.com in the address
                              bar and press the Enter key or scan the QR code.

                  STEP - 3   Click on the Get Started button.





























                                                               Building Projects with Google Teachable Machine
                                                                                                                    69]]></page><page Index="72"><![CDATA[A New Project  window will appear.  You  will see  three  types  of projects:  Image
                               Project, Audio Project and Pose Project.

                   STEP - 4   Click on the Image Project.






















                               A New Image Project dialog box will appear.

                   STEP - 5   Click on the Standard image model option.














                   STEP - 6   Click on the Class 1 and rename it to Pencil and Class 2 to Pen.

                   STEP - 7   Add two more classes by clicking on the Add a Class button. Rename these new
                               classes to Sharpener and Chair.
                   STEP - 8   Click on the Upload button inside the Pencil class.































                                 AI RoboGenius - VIII
                 70]]></page><page Index="73"><![CDATA[STEP - 9   Click on the Choose images from your files, or drag & drop here option.

































                              The Open window appears.


                 STEP - 10   Navigate to the folder on your computer where you saved your apple pictures.

                 STEP - 11   Select clear photos from your computer.

                 STEP - 12   Click on the Open button.































                              You will see the images appear as small thumbnails.

                              Similarly, upload images for the Pen, Sharpener and Chair classes in the same way
                              as the Pencil class.



                                                               Building Projects with Google Teachable Machine
                                                                                                                    71]]></page><page Index="74"><![CDATA[STEP - 13   Click on the Train Model button in the Training section, wait for 1-3 minutes for the
                               training to complete and make sure not to close the window during the process.





































                   STEP - 14   Click on the down arrow button in the Preview section.


                               A drop-down list appears.

                   STEP - 15   Click on the File option in the drop-down list.






































                                 AI RoboGenius - VIII
                 72]]></page><page Index="75"><![CDATA[STEP - 16   Click on the Choose images from your files, or drag & drop here option.







































                              The Open window appears.

                 STEP - 17   Navigate to the folder on your computer where you saved your pictures.

                 STEP - 18   Select a photo and click on the Open button to test your model.
































                              Check the confidence bars for predictions - high percentages (above 80%) indicate
                              your model is accurately trained.



                                                               Building Projects with Google Teachable Machine
                                                                                                                    73]]></page><page Index="76"><![CDATA[STEP - 19   Click on the Export button located at the top-right corner of the Preview section.

































                   STEP - 20   Click on the Export Model button after satisfying with the model’s accuracy. You will
                               see several options for exporting:

                                   Upload (shareable link): This option generates a shareable link to your model
                                   hosted by Google Teachable Machine.
                                   Download: This option allows you to download the model to your device.






































                               Once exported, you can use the model in your projects for real-time predictions.


                                 AI RoboGenius - VIII
                 74]]></page><page Index="77"><![CDATA[Use Google Teachable Machine for good projects.
                                                 Don't misuse it to invade people's privacy or create
                                                              unfair or harmful models.




                 AUDIO PROJECTS

                 Audio projects teach a model to recognise different sounds or words. Using a microphone, the
                 model listens to sounds such as claps, snaps, whistles or spoken words and learns to tell them
                 apart. As you record more examples, the model becomes better at understanding the differences
                 between each sound.

                 These projects are useful in many real-life situations. Audio projects can also be creative and fun.
                 You can make a model that turns on lights when you clap, plays a sound when you say a word
                 or recognises musical instruments like a guitar, flute or drum. This helps you to understand how
                 computers can hear, process and respond to sounds just like humans do.


                 Whistle vs. Clap Detector

                 To detect whistle and clap audio in Google Teachable Machine, follow the given steps:


                  STEP - 1   Click on the Audio Project.





































                  STEP - 2   Click on the Class 2 and rename it to Whistle.

                  STEP - 3   Add one  more class  by  clicking on  the  Add  a  Class  button  and  rename  it
                              to Clap.


                                                               Building Projects with Google Teachable Machine
                                                                                                                    75]]></page><page Index="78"><![CDATA[STEP - 4   Click on the Mic button inside the default Background Noise class. This teaches the
                               model what silence or normal background sound is.


































                   STEP - 5   Click on the Record 20 Seconds button to record background noise. You can also
                               change the duration by clicking on the Setting button.

                   STEP - 6   After  completing  the  recording,  click  on the  Extract  Sample button  to  add
                               audio samples.




































                               Similarly, add audio samples to class Whistle and Clap.



                                 AI RoboGenius - VIII
                 76]]></page><page Index="79"><![CDATA[STEP - 7   Click on the Train Model button.







































                              The system will now analyse all your audio samples (Background Noise, Whistle
                              and Clap) to learn their unique sound patterns.

                              Once training is finished, the button will change to say Model Trained.

                              The Preview section will now live-update. As it listens through your microphone,
                              it  will show a confidence  percentage  for each  sound  class  (Background  Noise,
                              Whistle and Clap).

































                                                               Building Projects with Google Teachable Machine
                                                                                                                    77]]></page><page Index="80"><![CDATA[STEP - 8   Click on the Export Model button after satisfying with the model’s accuracy. You will
                               see several options for exporting:

                                   Upload (shareable link): This option generates a shareable link to your model
                                   hosted by Google Teachable Machine.
                                   Download: This option allows you to download the model to your device.





































                  Once exported, you can use the model in your projects for real-time predictions.


                          VISUAL VAULT
                          Create an audio model that can recognise three different musical instruments.

                          1.                             2.                               3.









                                                         CHALLENGE CHAIN
                    What  challenges  might  you  face  when using  audio  models  with  multiple  sounds  in  the
                    environment? How could you address this?














                                 AI RoboGenius - VIII
                 78]]></page><page Index="81"><![CDATA[POSE PROJECTS

                 Pose projects let the model track body movements and positions using a webcam. It can learn
                 to identify actions like hands-up gestures, yoga poses or dance moves. These projects are
                 helpful for fitness tracking, counting  exercises, playing dance games  or controlling things
                 with gestures.
                 Pose projects also allow you to control things using gestures, such as moving a character in
                 a game by waving your hand or turning on music by lifting your arm. This makes learning
                 interactive and fun, showing how computers can understand body language just like humans.

                 Right-Hand and Left-Hand Recogniser                                     Pose projects use skeleton
                                                                               SMART
                 To recognise Right-hand and Left-hand  in Google              SPARK         tracking to follow
                 Teachable Machine, follow these steps:                                        body points.

                  STEP - 1   Click on the Pose Project.


























                  STEP - 2   Click on the Class 1 and rename it to Right-hand and Class 2 to Left-hand.


                  STEP - 3   Click on the Webcam button.



























                                                               Building Projects with Google Teachable Machine
                                                                                                                    79]]></page><page Index="82"><![CDATA[STEP - 4   Click on the Hold to Record button to record pose samples. You can also change the
                               duration by clicking on the Setting button.






























                               Similarly, add pose samples to class Left-hand.

                   STEP - 5   Click on the Train Model button.






























                               The system will now analyse all your images (Right-hand and Left-hand gestures)
                               to learn their unique visual patterns.


                                                          The Flip Camera option mirrors
                                                SMART
                                                 SPARK    the webcam view to make pose
                                                            training easier and natural.


                               Once training is finished, the button will change to say Model Trained.



                                 AI RoboGenius - VIII
                 80]]></page><page Index="83"><![CDATA[The Preview section will now live-update. As it detects your hand gestures through
                              the webcam, it will show a confidence percentage for each class (Right-hand and
                              Left-hand).



























                  STEP - 6   Click on the Export Model button  after satisfying with the model’s accuracy. You
                              will see several options for exporting:

                                  Upload (shareable link): This option generates a shareable link to your model
                                  hosted by Google Teachable Machine.
                                  Download: This option allows you to download the model to your device.
































                 Once exported, you can use the model in your projects for real-time predictions.


                      Ask AIRO

                      Create a pose model that recognises four different yoga poses using Google Teachable Machine.



                                                               Building Projects with Google Teachable Machine
                                                                                                                    81]]></page><page Index="84"><![CDATA[4.  Choose the section where you can test your trained model and see the confidence bars.
                   ECOTECH     Google Teachable Machine helps identify endangered species, aiding conservation efforts by
                   ENERGY      quickly classifying animals in photos.                                                                                       (i)  Training                             (ii)  Preview


                                                                                                                                                          (iii)  Export                              (iv)  Upload

                                                                                                                                                5.  Identify the  button that generates a shareable link or downloads your trained model.



                     «   Google Teachable Machine project is like teaching your computer to recognise                                                       (i)  Train Model                          (ii)  Record
                         patterns.
                                                                                                                                                          (iii)  Export Model                        (iv)  Add Class

                     «   Google Teachable Machine can be used to create real-world applications, allowing individuals to
                         explore and apply machine learning concepts.                                                                       B.  Write ‘T’ for true and ‘F’ for false.

                     «   Image projects help a computer recognise and classify images into different groups.                                    1.  The Pose Project helps track yoga poses or dance moves.

                     «   Google  Teachable  Machine  can be  combined  with augmented  reality  to  create  immersive                           2.  The Audio Project can detect claps and whistles.
                         real-time interactive experiences.
                                                                                                                                                3.  You can only create two classes in an Image Project.
                     «   Audio projects teach a model to recognise different sounds or spoken words.
                                                                                                                                                4.  The Train Model button allows the computer to learn from the examples provided
                     «   Pose projects let the model track body movements and positions using a webcam.
                                                                                                                                                   in the classes.
                     «   Pose projects also allow you to control things using gestures, such as moving a character in a
                         game by waving your hand or turning on music by lifting your arm.                                                      5.   Google Teachable Machine cannot be used to create educational model.

                                                                                                                                            C.  Fill in the blanks using the given hints:

                                                                                                                                                1.  Image  projects  help a computer  recognise  and  classify
                                                                                                                                                   into different groups.                                                              Hints
                                                            Robo Check                                                                          2.  Audio  projects  teach  a  model  to  recognise  different               or      Accurately

                                                                                                                                                   spoken words.                                                                      Images
                  A.  Tick ( ) the correct option.                                                                                                                                                                                    Webcam
                                                                                                                                                3.  The confidence bars show how                    your model is trained.           Augmented
                      1.   Which tool allows you to train a computer using examples like pictures or sounds?                                                                                                                         Reality (AR)
                                                                                                                                                4.  Pose projects track body movements and positions using a                   .      Sounds
                                  (i)  Google Classroom                     (ii)  Google Teachable Machine
                                                                                                                                                5.  Google  Teachable  Machine  can be  combined  with                       to
                                (iii)  Scratch                            (iv)  AI Assistant
                                                                                                                                                   create immersive experiences.

                      2.  Spot the correct example of how an audio model can be used.
                                                                                                                                            COMPETENCY-BASED QUESTIONS
                                  (i)  Giving voice commands                (ii)  Writing stories
                                                                                                                                            1.  An app developer wants to make a mobile app that responds when a user claps or whistles.
                                (iii)  Making animations                  (iv)  Solving puzzles
                                                                                                                                                Which project type should they use and why?
                      3.  Select the action that a pose project can recognise.                                                              2.  A factory wants to  separate  good  products from defective  ones automatically  using a

                                  (i)  Talking                              (ii)  Writing                                                       camera. Which project type should they use?

                                (iii)  Reading                            (iv)  Raising hands (hands-up gesture)




                                 AI RoboGenius - VIII
                 82]]></page><page Index="85"><![CDATA[4.  Choose the section where you can test your trained model and see the confidence bars.
 ECOTECH   Google Teachable Machine helps identify endangered species, aiding conservation efforts by
 ENERGY  quickly classifying animals in photos.     (i)  Training         (ii)  Preview


                               (iii)  Export                             (iv)  Upload

                    5.  Identify the  button that generates a shareable link or downloads your trained model.


                                (i)  Train Model                          (ii)  Record

                               (iii)  Export Model                       (iv)  Add Class

                 B.  Write ‘T’ for true and ‘F’ for false.


                    1.  The Pose Project helps track yoga poses or dance moves.

                    2.  The Audio Project can detect claps and whistles.

                    3.  You can only create two classes in an Image Project.


                    4.  The Train Model button allows the computer to learn from the examples provided
                        in the classes.

                    5.   Google Teachable Machine cannot be used to create educational model.


                 C.  Fill in the blanks using the given hints:

                    1.  Image  projects  help a computer  recognise  and  classify
                        into different groups.                                                             Hints

                    2.  Audio  projects  teach  a  model  to  recognise  different               or      Accurately
                        spoken words.                                                                      Images
                                                                                                          Webcam
                    3.  The confidence bars show how                    your model is trained.           Augmented
                                                                                                         Reality (AR)
                    4.  Pose projects track body movements and positions using a                   .       Sounds

                    5.  Google  Teachable  Machine  can be  combined  with                       to
                        create immersive experiences.



                 COMPETENCY-BASED QUESTIONS

                 1.  An app developer wants to make a mobile app that responds when a user claps or whistles.
                    Which project type should they use and why?

                 2.  A factory wants to  separate  good  products from defective  ones automatically  using a
                    camera. Which project type should they use?







                                                               Building Projects with Google Teachable Machine
                                                                                                                    83]]></page><page Index="86"><![CDATA[1.   Develop a model that detects facial expressions to identify emotions like happy, sad or surprised,
                     fostering emotional intelligence.

                  2.  Develop a model that identifies dance moves, allowing students to create interactive dance routines.








                  Goal



                  Setup






                  Observation





















                            People who are expert in  Google Teachable Machine can pursue their career as a Machine
                            Learning Engineer, AI Researcher or AI Educator.





























                                 AI RoboGenius - VIII
                 84]]></page><page Index="87"><![CDATA[maker mission


                                        Animal Sounds Detector in Google Teachable Machine

                 In this project, you'll build a voice detection model that can recognise different animal sounds using Google
                 Teachable Machine. This can be useful for educational tools, animal identification apps or even as a fun
                 project for learning about sound recognition.
                 To make  animal sounds  detector  model  in  Google  Teachable  Machine
                 follow the given steps:



                  STEP - 1    Select the Audio Project option on Google Teachable Machine.






                                                STEP - 2   Record clear audio  clips of animal sounds for  training the
                                                            model.





                  STEP - 3   Upload  the audio  clips  to  Google  Teachable Machine and
                              label them according to the animal sound.








                                               STEP - 4   Train your  model  using  the uploaded  audio  clips.  Google
                                                           Teachable Machine will process the sound files and learn to
                                                           distinguish the differences between the various animal noises.







                  STEP - 5   Test your model by playing  animal sounds and checking  if
                              the model correctly identifies the animal based on the sound.
                              Adjust the samples and retrain if necessary.




                                               STEP - 6     Once the model works well, export it and integrate it into an
                                                            app or project where it can detect animal sounds in real-
                                                            time.










                                                                                           Maker Mission
                                                                                                                    85]]></page><page Index="88"><![CDATA[6







                                                                                                        Sensors,


                                                                                                      Logic and



                                                                                        Maze Navigation






                  LEARNING OBJECTIVES


                     VR Robot’s Sensors                             Magnet in VR Programming
                    Console in VR Programming                       Logic in VR Programming
                    Programs using Different Playgrounds





                           AIRO, how do robots move in
                          VEXcode VR?




                                                                            They use sensors to detect objects
                                                                                        and measure distance.





                           So, they decide when
                           to turn or move?



                                                                                Yes, with logic blocks, they can
                                                                                          make such decisions.






                           That’s interesting!
                           Can I try it?


                                                                                   Of course! Let’s explore this
                                                                                                 in the chapter.







                                 AI RoboGenius - VIII
                 86]]></page><page Index="89"><![CDATA[This chapter introduces you to the fascinating world of VEXcode VR programming, where you will
                 learn how robots sense their surroundings, make logical decisions and move through complex mazes.


                 VR ROBOT’S SENSORS

                 In VEXcode VR, a sensor is a tool that helps the robot understand what’s around it. Sensors allow the
                 robot to gather information, so it can react to its environment. Here are the main types of sensors:

                 Sensing-Bumper
                 These sensors detect when the robot bumps into something. When this happens, the robot can
                 stop or change direction.

                 Some of the important Bumper sensing blocks are as follows:
                               Block                                          Description
                                                     This block checks if either the LeftBumper or RightBumper is
                    LeftBumper   pressed?
                                                     pressed, based on the selected option.
                                                     This block  triggers an action  when  the LeftBumper  or

                   when  LeftBumper    pressed       RightBumper is either pressed or released, depending on the
                                                     selected condition.


                 Sensing-Eye
                 These sensors help the robot see objects and colours in front of it and below it. The robot can use
                 this to recognise things or avoid obstacles.
                 Some of the important Eye sensing blocks are as follows:

                                Block                                          Description

                     FrontEye  is near object?         This block checks if an object is close in front of the robot.


                     FrontEye  detects  red  ?         This block detects a specific colour in the robot’s view.

                                                       This block measures the brightness of the surface directly in
                    FrontEye  brightness in %
                                                       front of the robot.

                                                       This block triggers an action when the Front Eye or Down
                   when FrontEye   detects  an object
                                                       Eye of Robot detects or or loses an object.


                 Sensing-Distance
                 This sensor measures how far an object is from the robot. It helps the robot avoid crashing into
                 things by telling it how much space is ahead.

                 Some of the important Distance sensing blocks are as follows:
                                 Block                                          Description

                                                        This block checks if the FrontDistance sensor detects any
                     FrontDistance  found an object?
                                                        object in front of the robot.
                                                        This block  measures  the  distance  from the  robot  to  an
                    FrontDistance  in  mm
                                                        object in millimetres, based on the sensor’s reading.

                                                                         Sensors, Logic and Maze Navigation
                                                                                                                    87]]></page><page Index="90"><![CDATA[Sensing-Location
                  This sensor tells the robot its exact position on the grid. It helps the robot know where it is and
                  move to specific location. Some of the important Location sensing blocks are as follows:

                              Block                                          Description

                                                 This block measures the robot’s position on the X and Y-axis in
                    position x  in  mm
                                                 millimetres.

                    position angle in degrees    This block measures the robot’s orientation or angle in degrees.



                  MAGNET IN VR PROGRAMMING

                  The Magnet block is used to control the magnet’s power in your
                  program. The block  has the option  to  set  the magnet’s  power        energize Magnet  to  boost
                  to  boost or  drop. If  you choose  boost, the magnet  becomes
                  energized and its power increases. If you choose drop, the magnet’s power is turned off or reduced.
                  This helps you control how the magnet behaves in your project.


                  CONSOLE IN VR PROGRAMMING
                  The Console blocks in VEXcode VR are used for displaying information, such as messages or
                  values, while the robot is running. It acts as a virtual display screen that shows the output of
                  your program. You can use the Console block to print information like numbers, sensor values
                  or messages, helping you track the robot’s status or program execution. Some of the important
                  Console blocks are as follows:

                               Block                                          Description


                     print  VEXcode                This block displays a message or value on the console.



                     set cursor to next row        This block moves the cursor to the next line in the console.



                     clear all rows                This block clears all the previous output from the console.


                                                   This block sets the number of decimal  places  the console  will
                     set print precision to  0.1
                                                   display for numbers.

                     set print color  black        This block changes the colour of the text displayed in the console.




                  LOGIC IN VR PROGRAMMING

                  In VEXcode VR, logic is used to create a set of instructions that guide the VR robot to perform
                  specific tasks. Logic helps control how the robot reacts to different situations and ensures it follows
                  a sequence of steps correctly. Here are the main types of logic:


                                 AI RoboGenius - VIII
                 88]]></page><page Index="91"><![CDATA[Logic–Control

                 Control blocks are used to decide when, how and how often the robot should carry out an action.
                 They enable the robot to make choices, repeat tasks and pause at the right times.
                 Some of the important Control blocks are as follows:

                             Block                                          Description

                   repeat  10                    This block repeats the enclosed code a fixed number of times,
                                                 allowing the robot to execute actions repeatedly for a specified
                                                 count.



                   forever                       This block repeats the enclosed code endlessly until the program
                                                 stops. It’s ideal for tasks that should run without interruption, like
                                                 maintaining movement or sensors.


                   while                         The code  inside  this  block  keeps  looping  as  long as  the  given
                                                 condition is true. It’s useful for continuous actions that depend on
                                                 real-time conditions.


                                                 This block  pauses  the  program  until  a  specified  condition
                   wait until                    becomes true. Once the condition is met, the program resumes
                                                 its execution.


                   if       then                 This block runs the code only when a specific condition is true. It’s
                                                 used to make the robot perform actions based on conditions that
                                                 must be met.


                   if      then
                                                 This block checks a condition; if the condition is true, it runs one set

                   else                          of instructions. If false, it runs a different set, offering a decision-
                                                 making path.




                    if         then

                                                 This block checks multiple conditions in order. It first checks if the
                    else if    then              first condition is true and runs the corresponding code. If not, it
                                                 checks  the next condition  and continues  until all  options  have
                                                 been evaluated, running the final set of instructions if none are
                    else
                                                 true.









                                                                         Sensors, Logic and Maze Navigation
                                                                                                                    89]]></page><page Index="92"><![CDATA[Logic–Operators

                  Operators are used to perform calculations within your program. These can be used to change
                  the values of variables, control movement speeds or check distances.
                  Some of the important Operators blocks are as follows:

                                Block                                          Description


                      0    +      0                  This block performs basic arithmetic operations, like addition
                                                     (+), subtraction (-), multiplication (*) and division (/).
                           =                         This block compares two values using operators like equal to
                      0           50
                                                     (==), less than (<) and greater than (>).
                                                     This block performs logical operations: and (true only if both
                           and
                                                     conditions are true) and or (true if at least one condition is true).

                     not                             This block reverses the truth value of a condition.

                                                     This block  compares  multiple  conditions using  operators  like
                      0    <      0    <      0      less  than  (<),  greater  than  (>),  less  than  or  equal  to  (≤)  and
                                                     greater than or equal to (≥).

                    convert  0  to  text             This block converts a number into either text or number format.


                        Ask AIRO

                       Make a program that uses a variable to count how many times the robot turns right.


                  Logic-Events

                  In  VEXcode  VR,  event blocks are used  to  start specific  actions  or behaviours  when  certain
                  conditions are met, like when an event happens or when the program begins. These blocks help
                  the robot react to inputs or events, making the programming more interactive and dynamic.
                  Some of the important Events blocks are as follows:

                                 Block                                          Description

                                                        This block is triggered automatically when the project starts.
                    when started
                                                        It begins the actions of the script when the program runs.

                                                        This block  executes  a  script  when the  specified event  is
                    when I receive my_event
                                                        received. You can trigger actions based on specific events.

                                                        This block broadcasts the event to all other scripts, notifying
                    broadcast my_event
                                                        them that the event has occurred.


                                                        This block broadcasts the event and pauses the script until
                    broadcast my_event  and wait
                                                        the event is received by other scripts before continuing.




                                 AI RoboGenius - VIII
                 90]]></page><page Index="93"><![CDATA[Logic–Variables

                 Variable blocks are used to store values like counts, scores or distances and track changes in a
                 project. They help control the robot’s behaviour based on these values. You can create a new
                 variable with the Make a Variable button, which acts as a container for storing and updating
                 values like numbers or text.
                 Some of the important Variables blocks are as follows:

                                Block                                          Description
                                                       This block  refers to  a custom  variable  that  you  create  in
                   myVariable
                                                       your project.


                   set  myVariable  to  1              This block assigns a value to the variable.


                                                       This block  increases  or decreases  (if you  use  a  negative
                    change  myVariable  by  1
                                                       number) the value of the variable by a specified amount.

                 Logic–Comments

                 Comment blocks are used to add notes or explanations within the code. These notes
                 are not executed by the program but help programmers understand the purpose of             comment
                 different parts of the code, making it easier to read and maintain.

                        VISUAL VAULT
                        Identify and explain the blocks given below:


                        1.     FrontEye   is near object?



                        2.   convert  0  to  text


                        3.      0   =      50






                 PROGRAMS USING DIFFERENT PLAYGROUNDS

                 In this section, you will create programs to help your robot move through different playgrounds.
                 Each playground  has a unique  layout  that  challenges you  to  plan and code  the robot’s
                 movement carefully.

                 Coral Reef Cleanup
                 In  the VEXcode  VR  simulation,  the  coral reef environment  teaches  about  the  importance  of
                 keeping oceans clean. The robot is programmed to collect trash from the seabed, showing how
                 technology can help protect marine ecosystems and promote environmental awareness.



                                                                         Sensors, Logic and Maze Navigation
                                                                                                                    91]]></page><page Index="94"><![CDATA[To clean the Coral Reef in VEXcode VR, follow these steps:

                    STEP - 1   Open your web browser, type https://vr.vex.com/ in the address bar and
                               press the Enter key or scan the QR code.

                   STEP - 2   Click on the SELECT PLAYGROUND button.

                   STEP - 3   Select the Coral Reef Cleanup playground.


























                               The when started block is already placed by default in the workspace.





























                   STEP - 4   Drag the forever block from the Logic-Control category and place it below the when
                               started block.

                   STEP - 5   Drag the if then else block from the Logic-Control category and place it inside the
                               forever block.


                   STEP - 6   Drag the FrontDistance found an object? block from the Sensing-Distance category
                               and place it into the condition space of the if block.



                                 AI RoboGenius - VIII
                 92]]></page><page Index="95"><![CDATA[STEP - 7   Drag the drive forward block from the Drivetrain category and place it inside the
                              if section.

                  STEP - 8   Drag the  turn right block  from the  Drivetrain category  and  place  it  inside  the
                              else section.





























                  STEP - 9   Click on the Start button to run the program.




























                 The robot begins moving through the coral reef to collect trash. It uses its Front Distance Sensor
                 to detect objects and moves forward to pick them up. When no object is found, it turns right to
                 explore further. The Trash Collected display shows the total weight of debris gathered. The robot
                 runs on a battery and when the battery level becomes low, it automatically stops working until
                 recharged.

                 When the battery  reaches 0%, a pop-up  appears showing the total  trash collected.  It has
                 two buttons:
                    Get Certificate button is used to generate your certificate.



                                                                         Sensors, Logic and Maze Navigation
                                                                                                                    93]]></page><page Index="96"><![CDATA[Try Again is used to restart the cleanup.








                                                                                       The default grid size in the
                                                                          SMART       Playground is 200 mm × 200
                                                                          SPARK
                                                                                            mm per square.













                                        Students should develop the ethical value of patience by learning to stay
                                         calm and focused while working through complex problems and they
                                           should avoid taking shortcuts or engaging in unethical practices.



                  Number Grid Map
                  The Number Grid Map helps you practise navigation and control using coordinates. Each square
                  on the grid has a number and your task is to move the robot from one numbered square to
                  another.

                  Move the robot on the Number Grid Map playground in a zig-zag pattern to reach number 100.

                  To do this, follow the given steps:

                    STEP - 1    Open your web browser, type https://vr.vex.com/ in the address bar and
                               press the Enter key or scan the QR code.

                   STEP - 2   Click on the SELECT PLAYGROUND button.
                               The selected playground will load in the workspace.



























                                 AI RoboGenius - VIII
                 94]]></page><page Index="97"><![CDATA[The when started block is already placed by default in the workspace.
                  STEP - 3    Click on the Make a Variable button in the Logic-Variables category, name it AIRO
                              and click on the Submit button.

                  STEP - 4   Drag the set AIRO to 0 block from the Logic-Variables category and place it below
                              the when started block.
                  STEP - 5   Drag the while block from the Logic-Control category and place it below the set AIRO
                              to 0 block.

                  STEP - 6    Set the condition to AIRO < 9 in the while block by using blocks from the Logic-
                              Variables and Logic-Operator categories.

                  STEP - 7   Drag the turn right for 90 degrees block from the Drivetrain category and place it
                              inside the while block.
                  STEP - 8   Drag the drive forward for 200 mm block from the Drivetrain category and place
                              it below the turn right for 90 degrees block.

                  STEP - 9   Drag the stop driving block from the Logic-Control category and place it below the
                              drive forward for 200 mm block.
                 STEP - 10   Drag the turn left for 90 degrees block from the Drivetrain category and place it
                              below the stop driving block.

                 STEP - 11   Drag another drive forward for 200 mm block and place it below the turn left for
                              90 degrees block.
                 STEP - 12   Drag the change AIRO by 1 block from the Logic-Variables category and place it
                              below the drive forward for 200 mm block.

                 STEP - 13   Drag the stop driving block from the Drivetrain category and place it after the while
                              block.

                 STEP - 14   Drag the stop project block from the Logic-Control category and place it below the
                              stop driving block.































                                                                         Sensors, Logic and Maze Navigation
                                                                                                                    95]]></page><page Index="98"><![CDATA[STEP - 15   Click on the Start button to run the program.






































                  The robot moves in a zigzag pattern across the Number Grid Map to reach number 100. It turns
                  right and drives forward across one row, then turns left to move to the next. Each time it completes
                  a row, the variable AIRO increases by one. This continues until the robot reaches the last number,

                  where it stops and ends the project.

                                                         CHALLENGE CHAIN

                    Modify your zig-zag program so the robot moves backward to return to its starting point after
                    reaching number 100.






















                   ECOTECH     With  the  help of VEXcode  VR,  students  can  simulate  pollution  control  by  programming
                   ENERGY      robots to monitor air or water quality.





                                 AI RoboGenius - VIII
                 96]]></page><page Index="99"><![CDATA[STEP - 15   Click on the Start button to run the program.



                    «   In VEXcode VR, a sensor is a tool that helps the robot understand what’s
                        around it.

                    «   The Magnet block is used to control the magnet’s power in your program.
                    «   The Console blocks in VEXcode VR are used for displaying information, such as messages or
                        values, while the robot is running.

                    «   In VEXcode VR, logic is used to create a set of instructions that guide the VR robot to perform
                        specific tasks.

                    «   Control blocks are used to decide when, how and how often the robot should carry out an action.

                    «   Operators are used to perform calculations within your program.
                    «   In VEXcode VR, event blocks are used to start specific actions or behaviours when certain.
                    «   Variable blocks are used to store values like counts, scores or distances and track changes in a
                        project.

                    «   Conditions are met, like when an event happens or when the program begins.
                    «   In the VEXcode VR simulation, the coral reef environment teaches students about the importance
                        of keeping oceans clean.


                    «   The Number Grid Map helps you practise navigation and control using coordinates.
 The robot moves in a zigzag pattern across the Number Grid Map to reach number 100. It turns
 right and drives forward across one row, then turns left to move to the next. Each time it completes
 a row, the variable AIRO increases by one. This continues until the robot reaches the last number,

 where it stops and ends the project.

 CHALLENGE CHAIN                                           Robo Check

 Modify your zig-zag program so the robot moves backward to return to its starting point after   A.  Tick ( ) the correct option.
 reaching number 100.
                    1.   Identify the sensor that helps the robot measure how far an object is.

                                (i)  Distance                             (ii)  Eye

                               (iii)  Bumper                             (iv)  Magnet

                    2.  Spot the block that displays a message or value on the Console.

                                (i)  set print color                      (ii)  clear all rows


                               (iii)  set cursor to next row             (iv)  print

                    3.  Mark the block that repeats the enclosed code endlessly until the program is stopped.

 ECOTECH   With  the  help of VEXcode  VR,  students  can  simulate  pollution  control  by  programming      (i)  repeat       (ii)  forever
 ENERGY  robots to monitor air or water quality.
                               (iii)  while                              (iv)  if then




                                                                         Sensors, Logic and Maze Navigation
                                                                                                                    97]]></page><page Index="100"><![CDATA[4.  Choose the block which is triggered automatically when the project starts.

                                  (i)  when I receive                       (ii)  broadcast

                                (iii)  when started                       (iv)  broadcast and wait

                      5.  Which block is used to add notes or explanations within the code?


                                  (i)  Variable                             (ii)  Event

                                (iii)  Comment                            (iv)  Control

                  B.  Write ‘T’ for true and ‘F’ for false.

                      1.  The Magnet block is used to control the magnet's power in your program.


                      2.  Repeat block repeats the enclosed code only once and then stops automatically.

                      3.  Control blocks are used to change the colour and speed of the robot’s movement.


                      4.  The myVariable block refers to a custom variable that you create in your project.

                      5.   Event blocks help the robot react to inputs or events, making the programming
                         more interactive and dynamic.


                  C.  Fill in the blanks using the given hints:

                      1.  In VEXcode VR, a                 is a tool that helps the robot understand
                         what’s around it.                                                                   Hints
                                                                                                          Boost, Drop
                      2.  Magnet block has the option to set the magnet’s power to                          Sensor
                         or                 .                                                              Operators
                                                                                                            Console
                      3.  The                  acts as a virtual display  screen that  shows the          Coordinates
                         output of your program.


                      4.                  are used to perform calculations within your program.
                      5.  The Number Grid Map helps you practise navigation and control using                      .



                  COMPETENCY-BASED QUESTIONS

                  1.  A student  wants to  program  a VEXcode  VR robot  to  collect  objects  from the  Coral Reef
                      Cleanup playground and avoid obstacles on its path. Which sensors should the student use
                      and why?

                  2.  Riya wants her VEXcode VR robot to stop whenever it touches a wall and then turn to move in
                      another direction. Which sensor should she use and how will it help?






                                 AI RoboGenius - VIII
                 98]]></page><page Index="101"><![CDATA[1.   Program to make the robot draw geometric shapes using the pen tool.

                 2.  Program to make the robot travel from one number to another, covering the entire grid.










                 Goal



                 Setup






                 Observation



















                          People  expert  in VEXcode VR  can pursue  their  careers as a Robotics  Engineer,  Software
                          Developer or STEM Educator.































                                                                         Sensors, Logic and Maze Navigation
                                                                                                                    99]]></page><page Index="102"><![CDATA[maker mission


                                              3D Hologram Projector from a Plastic Bottle

                  In this project, you’ll create a 3D hologram projector using a plastic bottle and transparent sheet. This
                  can be a fun way to learn about optical illusions, explore basic geometry and create your own interactive
                  hologram display using simple materials.
                  Let’s create a cool 3D hologram projector using everyday materials such as:

                     Plastic Bottle                             Scissors                     Ruler
                     Transparent plastic sheet                  Tape                         Smartphone or Tablet

                  To create a 3D Hologram Projector from a Plastic Bottle follow the
                  given steps:


                   STEP - 1    Get a plastic bottle, scissors, a ruler, a transparent
                               plastic sheet, tape and your smartphone or tablet.





                                                         STEP - 2   Cut the bottom of the plastic bottle to create a flat
                                                                     surface, leaving the top intact. This will serve as the
                                                                     base for your projector.





                   STEP - 3   Using the transparent plastic sheet, trace and cut
                               out one piece in the shape shown in the image (with
                               a straight top and curved sides). This will form the
                               sides of your pyramid.





                                                         STEP - 4   Tape  the  single  piece  into  a  3D pyramid  shape.
                                                                     Ensure  the  base  of the  pyramid  fits  over your
                                                                     smartphone or tablet screen.






                   STEP - 5   Place the pyramid upside down on your phone or
                               tablet, play a hologram video or app and watch the
                               3D image float inside.












                                 AI RoboGenius - VIII
                 100]]></page><page Index="103"><![CDATA[SMART CITY ROBOTICS SOLUTION


                                                                   Design and simulate a robotics-powered smart
                                                                   city  solution  that  uses  AI models,  sensors and
                                                                   automation to manage urban activities such as
                          CAPSTONE PROJECT                         waste collection, traffic control and environment
                                                                   monitoring.





                 Session 1: Robotics & AI Foundations

                 Activities:

                 1.  Understanding Robots & Ethics
                      Task: Use Google Slides to create a visual presentation explaining the 5 generations of robots
                    and their role in modern cities. Include an ethical dilemma (e.g., Should robots replace traffic
                    police?) and present your conclusion.
                    Skills Practised:
                        Characterising robot generations
                        Exploring and discussing robot ethics
                        Presenting information effectively

                 2.  Google Teachable Machine Model
                      Task: Train a Teachable Machine model to recognise 3 categories of urban waste (e.g. plastic
                    bottle, paper and fruit peel). Prepare it for use in a city waste-sorting robot.
                    Skills Practised:
                        Collecting and labelling image data
                        Training and testing an AI model

                        Exporting the model for integration or deployment

                      Outcome for Session 1: Build awareness about ethical, generational and functional aspects of
                    robots. Create an AI model that forms the core of an automated waste classification system.
                 Session 2: Smart Blocks in Action

                 Activities:

                 1.  Robot as a System Model
                      Task: Draw a block diagram showing the mechanical, electronic and computational blocks of
                    your Smart City Robot. Label all key parts and explain each.

                    Skills Practised:
                        Identifying robot components
                        Visualising system design
                        Explanatory writing


                                                                                         Capstone Project
                                                                                                                    101]]></page><page Index="104"><![CDATA[2.  Robotics Prototype Design

                       Task: Create  a paper  or cardboard  prototype of your  robot  (e.g. waste-collection  robot),
                     showing its parts: wheels, camera/vision sensor, controller, etc.
                     Skills Practised:
                         Prototyping and modelling
                         Logical sketching and presentation
                         Hands-on design thinking


                       Outcome for Session 2: Develop practical understanding of robot design through modelling
                     and identification of key blocks.


                  Session 3: Simulation and Automation

                  Activities:
                  1  VEXcode VR Navigation
                       Task: Code a VEX VR robot to navigate the Number Grid Map and reach specific city zones
                     such as Market, School or Park. Use sensors and conditional logic.

                     Skills Practised:
                         Using conditions and loops
                         Applying sensing commands
                         Controlling robot motion on a grid

                  2.  VEXcode VR Data Logger Task
                       Task: Create a block-based project that logs detected waste categories and recommends
                     disposal zones. Simulate detection with colour/number tiles.

                     Skills Practised:
                         Simulating input events with colour/number tiles
                         Mapping detected waste types to recommended disposal zones

                       Outcome for Session 3: Integrate virtual robotics programming with logic and data processing
                     to simulate smart city automation behaviour.

                  Final Deliverables
                  Tick ( ) the box if submitted:

                     Slides on robot generations & ethics


                     Trained Teachable Machine model

                     Block diagram of the Smart City Robot

                     Prototype design (paper model)


                     VEXcode VR project for path navigation

                     VEXcode VR project (.vrblocks) for the Data Logger


                                 AI RoboGenius - VIII
                 102]]></page><page Index="105"><![CDATA[components list



                                               A webcam is a small camera used for capturing video or images
                                               and transmitting them over the Internet. It is commonly used in
                                               video conferencing, online streaming and security systems.




                          Webcam


                                               A microphone captures sound and converts it into an electrical
                                               signal. It is widely used in audio recording, communication systems
                                               and sound detection devices.




                         Microphone

                                               A speaker converts electrical signals into sound waves, allowing
                                               audio  to  be heard. It is  commonly  used  in music  systems,
                                               computers and entertainment devices.




                           Speaker

                                               Sensors are like the robot’s eyes and ears. They help the robot
                                               understand what is around it. To move, a robot uses wheels or
                                               legs powered by motors. Some robots walk on legs, while others
                                               roll on wheels.



                           Sensor

                                               A magnet is a material that produces a magnetic field and attracts
                                               or repels other magnetic materials. Magnets are commonly used
                                               in motors, sensors and magnetic storage devices.





                           Magnet

                                               A VR robot combines virtual reality and robotics, allowing users
                                               to control a robot remotely or interact with a virtual robot in a
                                               simulated  environment.  It uses  VR  headsets  and controllers  to
                                               provide an immersive experience, making it useful for applications
                                               in education, training and remote tasks.
                    Virtual Reality Robot



                                                                                         Components List
                                                                                                                   103]]></page><page Index="106"><![CDATA[young innovators




                                                                 T Ravi Kiran

                                                                 T Ravi Kiran, originally from Telangana, has
                                                                 established himself as a rising figure in assistive
                                                                 technology. At a young age he founded and leads a
                                                                 venture called Alien Innovations Pvt Ltd, where he
                                                                 has channelled his interest in robotics and AI toward
                                                                 real world accessibility solutions. One of his standout
                                                                 creations is a device named “BlindEye”, a wearable
                                                                 tool designed for visually impaired individuals. This
                                                                 innovation  employs  sensors,  cameras  and audio
                                                                 feedback to detect obstacles, recognise objects, read
                                                                 text  aloud  and perform other  assistive  functions.
                                                                 Ravi’s work earned him global recognition: he was
                     among the winners of the 2023 imaGen Ventures Global Youth Challenge powered by UNICEF
                     and has received support for scaling his invention. Ravi’s journey is marked by early exposure
                     to tinkering and robotics, which he combined with empathy for people’s needs. He has gathered
                     insights  directly  from persons  with  visual  impairment  to  shape  features  of BlindEye.  His  work
                     illustrates how youthful curiosity, paired with a social impact mindset, can lead to meaningful
                     technological solutions.

                     Hemesh Chadalavada
                     Hemesh  Chadalavada,  based  in  Hyderabad,  is
                     another exceptional  young innovator.  He turned
                     personal experience  into  invention  when,  at the
                     age of 12 or so, he saw his grandmother’s struggles
                     with  Alzheimer’s  and  dementia:  for instance,  she
                     left the gas on  while preparing tea.  Motivated  by
                     this,  he  developed  a  wearable  device  called  the
                     AlphaMonitor.  This is  designed  to  assist  elderly
                     people  with dementia  by tracking  gait,  posture,
                     body temperature and pulse and by triggering alerts
                     when  falls  occur or wandering happens. Hemesh
                     has received several awards for  his innovation:
                     the Pradhan Mantri Rashtriya Bal Shakti Puraskar (for young achievers under 18) and others via
                     large scale innovation challenges. His invention is close to commercialisation and his ambition is to
                     continue his education abroad in robotics, while making a positive impact back home.



                                 AI RoboGenius - VIII
                 104]]></page></pages></Search>
