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Duration 21 hours
Course Outline
Introduction to Robot Learning
- Overview of machine learning applications in robotics
- Distinguishing supervised, unsupervised, and reinforcement learning
- RL applications in control, navigation, and manipulation
Foundations of Reinforcement Learning
- Markov decision processes (MDP)
- Understanding policy, value, and reward functions
- Balancing exploration and 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 methods
- Advanced algorithms: A3C, DDPG, and PPO
Simulation Platforms for Robot Learning
- Utilizing 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 RL to robotic manipulation tasks
- Multi-agent reinforcement learning for swarm robotics
Optimization, Deployment, and Real-World Application
- Hyperparameter tuning and reward shaping
- Transferring learned policies from simulation to reality (Sim2Real)
- Deploying trained models on physical robotic hardware
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental machine learning concepts
- Proficiency in Python programming
- Knowledge of robotics and control systems
Target Audience
- Machine learning engineers
- Robotics researchers
- Developers focused on building 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.
