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Duration 21 hours
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
Core Concepts of Reinforcement Learning
- Markov decision processes (MDP)
- Understanding policy, value, and reward functions
- Managing exploration vs. exploitation trade-offs
Traditional RL Algorithms
- Q-learning and SARSA
- Monte Carlo and temporal difference methods
- Value iteration and policy iteration
Deep Reinforcement Learning Methods
- Integrating deep learning with RL (Deep Q-Networks)
- Policy gradient approaches
- Advanced algorithms: A3C, DDPG, and PPO
Simulation Environments for Robot Learning
- Leveraging OpenAI Gym and ROS 2 for simulation
- Creating custom environments for specific robotic tasks
- Assessing performance and training stability
Applying RL in Robotics
- Developing control and motion policies
- Using RL for robotic manipulation
- Multi-agent reinforcement learning in swarm robotics
Optimization, Deployment, and Real-World Integration
- Tuning hyperparameters and shaping rewards
- Transferring learned policies from simulation to reality (Sim2Real)
- Deploying trained models on robotic hardware
Conclusion and Future Directions
Requirements
- A solid understanding of fundamental machine learning concepts
- Proficiency in Python programming
- Basic knowledge of robotics and control systems
Target Audience
- Machine learning engineers
- Robotics researchers
- Developers creating intelligent robotic systems
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.
