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Course Outline
Introduction to Robot Learning
- Overview of machine learning within robotics.
- Distinctions 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.
- Balancing exploration versus exploitation trade-offs.
Classical RL Algorithms
- Q-learning and SARSA methods.
- Monte Carlo and temporal difference techniques.
- Value iteration and policy iteration strategies.
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
- Utilising OpenAI Gym and ROS 2 for simulation purposes.
- Constructing custom environments tailored to robotic tasks.
- Assessing performance and training stability.
Applying RL to Robotics
- Acquiring control and motion policies.
- Employing reinforcement learning for robotic manipulation.
- Implementing multi-agent reinforcement learning in swarm robotics.
Optimization, Deployment, and Real-World Integration
- Hyperparameter tuning and reward shaping techniques.
- Transferring learned policies from simulation to reality (Sim2Real).
- Deploying trained models onto robotic hardware.
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts.
- Practical experience with Python programming.
- Familiarity with robotics and control systems.
Target Audience
- Machine learning engineers.
- Robotics researchers.
- Developers focused on building 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.
