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Duration 21 hours
Course Outline
Introduction to Robot Learning
- The role of machine learning in robotics
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Real-world applications in control, navigation, and manipulation
Foundations of Reinforcement Learning
- Markov decision processes (MDP)
- Core components: policies, value functions, and reward structures
- Balancing exploration and exploitation
Traditional RL Algorithms
- Q-learning and SARSA approaches
- Monte Carlo methods and temporal difference learning
- Value and policy iteration techniques
Deep Reinforcement Learning Methods
- Synthesizing deep learning with RL (Deep Q-Networks)
- Policy gradient approaches
- Advanced algorithms: A3C, DDPG, and PPO
Simulation Platforms for Robot Learning
- Leveraging OpenAI Gym and ROS 2 for simulated testing
- Creating custom environments for specific robotic tasks
- Assessing performance metrics and training stability
Implementing RL in Robotics
- Developing control and motion strategies
- Applying reinforcement learning to robotic manipulation
- Multi-agent reinforcement learning for swarm robotics
Optimization, Deployment, and Real-World Application
- Tuning hyperparameters and shaping rewards
- Transferring learned policies from simulation to physical reality (Sim2Real)
- Deploying trained models on actual robotic hardware
Summary and Future Directions
Requirements
- A solid grasp of fundamental machine learning concepts
- Proficiency in Python programming
- Knowledge of robotics principles 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.
