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Duration 21 hours
Course Outline
Introduction to Robot Learning
- The role of machine learning in robotics
- Distinguishing supervised, unsupervised, and reinforcement learning
- RL applications in control, navigation, and manipulation
Core Concepts of Reinforcement Learning
- Markov decision processes (MDP)
- Policies, value functions, and reward mechanisms
- Balancing exploration and exploitation
Traditional RL Algorithms
- Q-learning and SARSA
- Monte Carlo and temporal difference methods
- Value and policy iteration techniques
Deep Reinforcement Learning Methods
- Merging deep learning with RL (Deep Q-Networks)
- Policy gradient approaches
- Sophisticated algorithms: A3C, DDPG, and PPO
Simulation Platforms for Robot Learning
- Leveraging OpenAI Gym and ROS 2 for simulation
- Creating bespoke environments for robotic tasks
- Assessing performance and training stability
Implementing RL in Robotics
- Acquiring control and motion policies
- RL strategies for robotic manipulation
- Multi-agent RL in swarm robotics contexts
Optimization, Deployment, and Real-World Application
- Tuning hyperparameters and shaping rewards
- Migrating learned policies from simulation to reality (Sim2Real)
- Deploying trained models on physical robotic hardware
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning concepts
- Proficiency in Python programming
- Knowledge of robotics and control systems
Audience
- Machine learning engineers
- Robotics researchers
- Developers creating 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.
