AI+ Robotics Practitioner™

Course structure

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