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Course Outline
Introduction to Robot Learning
- Overview of machine learning in robotics.
- Differences between supervised, unsupervised, and reinforcement learning.
- Applications of RL in control, navigation, and manipulation.
Fundamentals of Reinforcement Learning
- Markov decision processes (MDP).
- Policy, value, and reward functions.
- Exploration versus exploitation trade-offs.
Classical RL Algorithms
- Q-learning and SARSA.
- Monte Carlo and temporal difference methods.
- Value iteration and policy iteration.
Deep Reinforcement Learning Techniques
- Integrating deep learning with RL (Deep Q-Networks).
- Policy gradient methods.
- Advanced algorithms: A3C, DDPG, and PPO.
Simulation Environments for Robot Learning
- Utilizing OpenAI Gym and ROS 2 for simulation.
- Creating custom environments tailored for robotic tasks.
- Evaluating performance and training stability.
Applying RL to Robotics
- Learning control and motion policies.
- Reinforcement learning for robotic manipulation.
- Multi-agent reinforcement learning in swarm robotics.
Optimization, Deployment, and Real-World Integration
- Hyperparameter tuning and reward shaping.
- Transferring learned policies from simulation to reality (Sim2Real).
- Deploying trained models on robotic hardware.
Summary and Next Steps
Requirements
- A clear understanding of machine learning concepts.
- Practical experience with Python programming.
- Familiarity with robotics and control systems.
Audience
- Machine learning engineers.
- Robotics researchers.
- Developers constructing intelligent robotic systems.
21 Hours
Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.
