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Course Outline
Introduction to Robot Learning
- Overview of machine learning in robotics.
- Comparison of supervised, unsupervised, and reinforcement learning.
- Applications of RL in control, navigation, and manipulation.
Reinforcement Learning Fundamentals
- Markov decision processes (MDP).
- Policy, value, and reward functions.
- Balancing exploration versus exploitation.
Classical RL Algorithms
- Q-learning and SARSA.
- Monte Carlo and temporal difference methods.
- Value iteration and policy iteration.
Deep Reinforcement Learning Techniques
- Merging 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 for robotic tasks.
- Assessing performance and training stability.
Applying RL to Robotics
- Acquiring control and motion policies.
- Applying reinforcement learning to 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 onto robotic hardware.
Summary and Next Steps
Requirements
- Knowledge of machine learning concepts.
- Proficiency in Python programming.
- Familiarity with robotics and control systems.
Target Audience
- Machine learning engineers.
- Robotics researchers.
- Developers creating 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.
