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Course Outline
Intro to Robot Learning
- Broad overview of 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)
- 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 methods
- Advanced algorithms: A3C, DDPG, and PPO
Simulation Platforms for Robot Learning
- Utilizing OpenAI Gym and ROS 2 for simulation
- Creating custom environments for specific robotic tasks
- Assessing performance metrics and training stability
Implementing RL in Robotics
- Acquiring control and motion policies
- Applying RL for robotic manipulation
- Multi-agent reinforcement learning in swarm robotics
Optimization, Deployment, and Practical Integration
- Hyperparameter tuning and reward shaping
- Transferring learned policies from simulation to reality (Sim2Real)
- Deploying trained models onto physical robotic hardware
Recap and Future Directions
Requirements
- Foundational knowledge of machine learning concepts
- Proficiency in Python programming
- Basic familiarity with robotics and control systems
Target Audience
- Machine learning engineers
- Robotics researchers
- Developers engineering 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.