AI+ Robotics™

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Robotics Engineers Enhance robotic system design and functionality using AI for automation and control.

Mechanical Engineers: Integrate AI to optimize robotics systems and improve performance in manufacturing and production.

AI Specialists: Apply AI techniques to enhance the intelligence and autonomy of robotic systems.

IT Specialists & System Integrators: Implement AI-powered solutions to improve robotics infrastructure and communication systems.

Students & New Graduates: Build essential skills in AI and robotics to succeed in an emerging field with endless growth potential.

Struktura kurzu

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

Certifikace

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