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Course Outline
Introduction to Robot Learning
- An overview of machine learning applications in robotics
- Distinguishing between supervised, unsupervised, and reinforcement learning
- RL applications in control, navigation, and manipulation
Foundations of Reinforcement Learning
- Markov decision processes (MDP)
- Understanding policies, value functions, and reward mechanisms
- Managing exploration versus exploitation trade-offs
Traditional RL Algorithms
- Q-learning and SARSA
- Monte Carlo methods and temporal difference learning
- Value iteration and policy iteration techniques
Advanced Deep Reinforcement Learning
- Fusion of deep learning and RL (Deep Q-Networks)
- Policy gradient methodologies
- Advanced algorithms such as A3C, DDPG, and PPO
Simulation Platforms for Robot Learning
- Leveraging OpenAI Gym and ROS 2 for simulation tasks
- Creating custom environments for specific robotic challenges
- Assessing performance metrics and training stability
Implementing RL in Robotics
- Acquiring control and motion policies
- Applying reinforcement learning to robotic manipulation
- Multi-agent reinforcement learning in swarm robotics
Optimization, Deployment, and Real-World Application
- Hyperparameter tuning and reward shaping strategies
- Transferring learned policies from simulation to the real world (Sim2Real)
- Implementing 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 specializing in 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.
