AI+ Robotics™

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

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Incluye:

Candidatos ideales para este curso:

AI Robotics Integration Expert:. Integrates AI technologies into existing robotic systems, enhancing their performance and enabling new functionalities and applications.
AI Robotics System Developer:. Creates complex robotic systems incorporating AI, focusing on enhancing capabilities like perception, learning, and adaptive behavior.
Robotics Engineer with AI Expertise:. Designs and develops advanced robots, integrating AI algorithms to enhance autonomy, decision-making, and overall robotic functionality.
AI Intelligent Robotics Specialist:. Specializes in developing intelligent robots that utilize AI for advanced tasks, such as navigation, manipulation, and human interaction.

Prerrequisitos

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

Contenido

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 .