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Course Outline
Introduction to Robot Learning
- Overview of machine learning applications in robotics
- Comparing supervised, unsupervised, and reinforcement learning
- RL applications in control, navigation, and manipulation
Fundamentals of Reinforcement Learning
- Markov decision processes (MDP)
- Understanding policies, 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
- Constructing custom environments for specific robotic tasks
- Assessing performance and training stability
Applying RL to Robotics
- Mastering control and motion policies
- RL techniques 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 onto robotic hardware
Summary and Next Steps
Requirements
- Familiarity with machine learning concepts
- Proficiency in Python programming
- Knowledge of 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.
