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Duration 21 hours
Course Outline
Introduction to Robot Learning
- Machine learning applications within robotics
- Comparing supervised, unsupervised, and reinforcement learning
- Use cases in control, navigation, and manipulation
Core Concepts of Reinforcement Learning
- Markov decision processes (MDP)
- Understanding policy, value, and reward functions
- Balancing exploration versus exploitation
Traditional RL Algorithms
- Q-learning and SARSA
- Monte Carlo and temporal difference methods
- Value iteration and policy iteration
Deep Reinforcement Learning Methods
- Merging deep learning with RL (Deep Q-Networks)
- Policy gradient techniques
- Advanced approaches: A3C, DDPG, and PPO
Simulated Environments for Robot Learning
- Leveraging OpenAI Gym and ROS 2 for simulations
- Creating custom environments for specific robotic tasks
- Assessing performance metrics and training stability
Implementing RL in Robotics
- Acquiring control and motion policies
- Applying reinforcement learning to robotic manipulation
- Multi-agent reinforcement learning for swarm robotics
Optimization, Deployment, and Real-World Integration
- Tuning hyperparameters and shaping rewards
- Transferring learned policies from simulation to the real world (Sim2Real)
- Deploying trained models onto physical robotic hardware
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning concepts
- Proficiency in Python programming
- Working knowledge of robotics and control systems
Target Audience
- Machine learning engineers
- Robotics researchers
- Developers focused on 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.
