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Duration 21 hours
Course Outline
Introduction to Robot Learning
- Overview of machine learning applications in robotics
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Practical uses of RL in control, navigation, and manipulation
Fundamentals of Reinforcement Learning
- Markov Decision Processes (MDP)
- Understanding policies, value functions, and reward mechanisms
- Balancing exploration versus exploitation strategies
Classical RL Algorithms
- Q-learning and SARSA techniques
- Monte Carlo methods and temporal difference learning
- Value iteration and policy iteration approaches
Deep Reinforcement Learning Techniques
- Integrating deep learning with RL via 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
- Assessing performance metrics and training stability
Applying RL to Robotics
- Developing control and motion policies
- Using RL for robotic manipulation tasks
- Multi-agent RL applications in swarm robotics
Optimization, Deployment, and Real-World Integration
- Hyperparameter tuning and reward shaping strategies
- Transferring learned policies from simulation to reality (Sim2Real)
- Deploying trained models on physical robotic hardware
Summary and Next Steps
Requirements
- A solid grasp of core 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.
