AI+ Robotics™

Build the Future with Smart Automation

* AI-Driven Robotics: Apply AI in Deep Learning, Reinforcement Learning, and smart automation
* Real-World Systems: Work with autonomous systems and intelligent agents
* Ethics & Innovation: Learn industry-aligned practices and innovation strategies
* Hands-On Projects: Gain experience designing, optimising, and deploying robotics solutions

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

Exam: 50 questions, 70% passing, 90 minutes, online proctored exam

Instructor-led OR Self-paced course + Official exam + Digital badge