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Duration 21 hours
Course Outline
Introductory Concepts in Robot Learning
- The role of machine learning within robotics
- Comparing supervised, unsupervised, and reinforcement learning
- RL applications in control, navigation, and object manipulation
Core Principles of Reinforcement Learning
- Markov decision processes (MDP)
- Defining policy, value, and reward functions
- Balancing exploration against exploitation
Traditional RL Methodologies
- Q-learning and SARSA techniques
- Monte Carlo and temporal difference strategies
- Value iteration and policy iteration processes
Deep Reinforcement Learning Approaches
- Integrating deep learning with RL (Deep Q-Networks)
- Policy gradient methodologies
- Advanced frameworks: A3C, DDPG, and PPO
Simulated Environments for Robot Education
- Leveraging OpenAI Gym and ROS 2 for simulation
- Creating bespoke environments for specific robotic tasks
- Assessing performance metrics and training stability
Implementing RL in Robotics
- Acquiring control and motion strategies
- Utilizing RL for robotic manipulation
- Multi-agent RL in swarm robotics
Refinement, Deployment, and Real-World Application
- Adjusting hyperparameters and shaping rewards
- Transferring learned policies from simulation to physical reality (Sim2Real)
- Implementing trained models on physical robotic hardware
Conclusions and Future Directions
Requirements
- Proficiency in core machine learning concepts
- Proficiency in Python programming
- Knowledge of robotics and control systems
Target Audience
- Machine learning engineers
- Robotics researchers
- Developers focused on 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.
