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Duration 21 hours
Course Outline
Introduction to Robot Learning
- 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)
- Definitions of policy, value, and reward functions
- Managing the exploration vs exploitation balance
Traditional RL Algorithms
- Q-learning and SARSA methodologies
- Monte Carlo and temporal difference learning
- Value and policy iteration techniques
Deep Reinforcement Learning Methods
- Fusing 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 metrics and training stability
Applying RL to Robotics
- Developing control and motion policies
- RL for enhancing robotic manipulation
- Multi-agent RL in swarm robotics contexts
Optimization, Deployment, and Real-World Application
- Tuning hyperparameters and shaping rewards
- Migrating learned policies from simulation to reality (Sim2Real)
- Deploying trained models onto physical robotic hardware
Conclusion and Future Directions
Requirements
- Solid understanding 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
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.
