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Duration 21 hours
Course Outline
Introduction to Robot Learning
- 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 policies, value, and reward functions
- Balancing exploration and exploitation trade-offs
Traditional RL Algorithms
- Q-learning and SARSA methodologies
- Monte Carlo and temporal difference techniques
- Value iteration and policy iteration processes
Advanced Deep Reinforcement Learning Methods
- Fusing deep learning with RL (Deep Q-Networks)
- Policy gradient approaches
- Advanced algorithms including A3C, DDPG, and PPO
Simulation Platforms for Robot Learning
- Leveraging OpenAI Gym and ROS 2 for simulation purposes
- Creating custom environments for specific robotic tasks
- Assessing performance metrics and training stability
Applying RL in Robotics
- Developing control and motion policies through learning
- Using reinforcement learning for robotic manipulation tasks
- Multi-agent reinforcement learning in swarm robotics
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
Wrap-up and Future Directions
Requirements
- A solid grasp of fundamental machine learning concepts
- Proficiency in Python programming
- Working 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.
