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Course Outline
Introduction to Robot Learning
- Overview of machine learning applications in robotics
- Comparing supervised, unsupervised, and reinforcement learning
- Applications of RL in control, navigation, and manipulation
Fundamentals of Reinforcement Learning
- Markov decision processes (MDP)
- Understanding policy, value, and reward functions
- Balancing exploration and exploitation trade-offs
Classical RL Algorithms
- Q-learning and SARSA
- Monte Carlo and temporal difference methods
- Value iteration and policy iteration
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
- Leveraging OpenAI Gym and ROS 2 for simulation
- Creating custom environments for specific robotic tasks
- Evaluating performance and training stability
Applying RL to Robotics
- Learning control and motion policies
- Using reinforcement learning 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 on physical robotic hardware
Summary and Next Steps
Requirements
- A solid understanding of machine learning concepts
- Proficiency in Python programming
- Familiarity with robotics and control systems
Target Audience
- Machine learning engineers
- Robotics researchers
- Developers 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.
