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
Tools you will explore
- OpenAI Gym
- GreyOrange
- Neurala
- Dialogflow
