AI+ Robotics™
Build the Future with Smart Automation
This course explores AI-driven robotics, enabling learners to apply AI techniques such as Deep Learning, Reinforcement Learning, and intelligent automation. It emphasizes real-world systems, providing experience with autonomous systems and intelligent agents. Learners will also examine ethics and innovation, understanding industry-aligned practices and strategies that shape responsible AI development. Through hands-on projects, participants gain practical experience in designing, optimising, and deploying AI-powered robotics solutions.
Enrollment Fee:
MUR 15,145
What You'll Learn
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
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
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)
4.1 Python for Robotics and Machine Learning
4.2 TensorFlow and PyTorch for AI in Robotics
4.3 Introduction to Other Essential Frameworks
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
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
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
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
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
10.1 Integration of Blockchain and Robotics
10.2 Quantum Computing and Its Potential
11.1 Understanding Robotic Process Automation and its use cases
11.2 Popular RPA Tools and Their Features
11.3 Integrating AI with RPA
12.1 Ethical Considerations in AI and Robotics
12.2 Safety Standards for AI-Driven Robotics
12.3 Discussion: Navigating AI Policies and Regulations
13.1 Latest Innovations in Robotics and AI
13.2 Future of Work and Society: Impact of AI and 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
Prerequisites
- Familiarity with basic concepts of Artificial Intelligence (AI), without the need for technical expertise.
- Openness to generate innovative ideas and concepts, leveraging AI tools effectively in the process.
- Ability to analyze information critically and evaluate the implications of AI and Robotics technologies.
- Readiness to engage in problem-solving activities and apply AI techniques to real-world scenario
Exam Details
Duration:
90 minutes
Passing Score:
70% (35/50)
Format:
50 multiple-choice/multiple-response questions
Delivery Method:
Online via proctored exam platform (flexible scheduling)
Unlock Self-Paced Online Learning
- Unlock Self-Paced Online Learning
- Access learning anytime, anywhere, with built-in quizzes to measure progress.
- Enrollment Fee: MUR 15,145