AI+ Robotics™

Module 1: Introduction to Robotics and Artificial Intelligence (AI) .

• 1.1 Overview of Robotics: Introduction, History, Evolution, and Impact 
• 1.2 Introduction to Artificial Intelligence (AI) in Robotics 
• 1.3 Fundamentals of Machine Learning (ML) and Deep Learning 
• 1.4 Role of Neural Networks in Robotics 

Module 2: Understanding AI and Robotics Mechanics .

• 2.1 Components of AI Systems and Robotics 
• 2.2 Deep Dive into Sensors, Actuators, and Control Systems 
• 2.3 Exploring Machine Learning Algorithms in Robotics

Module 3: Autonomous Systems and Intelligent Agents .

• 3.1 Introduction to Autonomous Systems 
• 3.2 Building Blocks of Intelligent Agents 
• 3.3 Case Studies: Autonomous Vehicles and Industrial Robots 
• 3.4 Key Platforms for Development: ROS (Robot Operating System) 

Module 4: AI and Robotics Development Frameworks .

• 4.1 Python for Robotics and Machine Learning 
• 4.2 TensorFlow and PyTorch for AI in Robotics 
• 4.3 Introduction to Other Essential Frameworks 

Module 5: Deep Learning Algorithms in Robotics .

• 5.1 Understanding Deep Learning: Neural Networks, CNNs 
• 5.2 Robotic Vision Systems: Object Detection, Recognition 
• 5.3 Hands-on Session: Training a CNN for Object Recognition 
• 5.4 Use-case: Precision Manufacturing with Robotic Vision 

Module 6: Reinforcement Learning in Robotics .

• 6.1 Basics of Reinforcement Learning (RL) 
• 6.2 Implementing RL Algorithms for Robotics 
• 6.3 Hands-on Session: Developing RL Models for Robots 
• 6.4 Use-case: Optimizing Warehouse Operations with RL 

Module 7: Generative AI for Robotic Creativity .

• 7.1 Exploring Generative AI: GANs and Applications 
• 7.2 Creative Robots: Design, Creation, and Innovation 
• 7.3 Hands-on Session: Generating Novel Designs for Robotics 
• 7.4 Use-case: Custom Manufacturing with AI 

Module 8: Natural Language Processing (NLP) for Human-Robot Interaction .

• 8.1 Introduction to NLP for Robotics 
• 8.2 Voice-Activated Control Systems 
• 8.3 Hands-on Session: Creating a Voice-command Robot Interface 
• 8.4 Case-Study: Assistive Robots in Healthcare 

Module 9: Practical Activities and Use-Cases .

• 9.1 Hands-on Session-1: Building AI Models for Object Recognition using Python Programming 
• 9.2 Hands-on Session-2: Path Planning, Obstacle Avoidance, and Localization Implementation using Python Programming 
• 9.3 Hands-on Session-3: PID Controller Implementation using Python programming 
• 9.4 Use-cases: Precision Agriculture, Automated Assembly Lines 

Module 10: Emerging Technologies and Innovation in Robotics .

• 10.1 Integration of Blockchain and Robotics 
• 10.2 Quantum Computing and Its Potential 

Module 11: Exploring AI with Robotic Process Automation .

• 11.1 Understanding Robotic Process Automation and its use cases 
• 11.2 Popular RPA Tools and Their Features 
• 11.3 Integrating AI with RPA 

Module 12: AI Ethics, Safety, and Policy.

• 12.1 Ethical Considerations in AI and Robotics 
• 12.2 Safety Standards for AI-Driven Robotics 
• 12.3 Discussion: Navigating AI Policies and Regulations 

Module 13: Innovations and Future Trends in AI and Robotics .

• 13.1 Latest Innovations in Robotics and AI 
• 13.2 Future of Work and Society: Impact of AI and Robotics 

Optional Module: AI Agents for Robotics.

1. 1. What Are AI Agents
2. 2. Key Capabilities of AI Agents in Robotics
3. 3. Applications and Trends for AI Agents in Robotics
4. 4. How Does an AI Agent Work
5. 5. Core Characteristics of AI Agents
6. 6. The Future of AI Agents in Robotics
7. 7. Types of AI Agents