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Course Outline
Introduction to AI Agents in Robotics
- Overview of AI applications in robotics.
- Types of AI agents used in robotic systems.
- Challenges associated with integrating AI and robotics.
Machine Learning and AI for Robotics
- Reinforcement learning for robotic control.
- Supervised and unsupervised learning for robot decision-making.
- Transfer learning and domain adaptation in robotics.
AI-Driven Perception and Sensing
- Computer vision for robotic perception.
- Sensor fusion and data processing.
- AI-enhanced object detection and recognition.
Autonomous Navigation and Path Planning
- AI-based obstacle avoidance.
- Path planning using deep learning.
- Simulating autonomous navigation in Gazebo.
Human-AI Collaboration in Robotics
- Understanding human-robot interaction.
- Developing assistive and cooperative robotic systems.
- Ethical and safety considerations.
Industrial and Service Robotics with AI
- AI applications in manufacturing and logistics.
- AI-driven robotic process automation (RPA).
- Future trends in the integration of AI and robotics.
Deploying AI-Powered Robotics Systems
- Optimizing AI models for real-world robotics applications.
- Deploying AI-driven robotic solutions in production environments.
- Evaluating system performance and adaptability.
Summary and Next Steps
Requirements
- A strong understanding of AI and machine learning principles.
- Experience with robotics frameworks such as ROS.
- Proficiency in Python or C++ for AI-driven robotics applications.
Audience
- Robotics engineers.
- AI researchers.
- Automation specialists.
21 Hours
