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Duration 21 hours
Course Outline
Foundations of Robot Learning
- The role of machine learning in robotics
- Comparing supervised, unsupervised, and reinforcement learning
- RL applications in control, navigation, and manipulation
Core Concepts of Reinforcement Learning
- Markov decision processes (MDP)
- Defining policies, values, and rewards
- Balancing exploration against exploitation
Traditional RL Algorithms
- Q-learning and SARSA methods
- Monte Carlo and temporal difference techniques
- Value iteration and policy iteration
Deep Reinforcement Learning Approaches
- Merging deep learning with RL (Deep Q-Networks)
- Policy gradient methodologies
- Advanced algorithms: A3C, DDPG, and PPO
Simulation Platforms for Robot Training
- Leveraging OpenAI Gym and ROS 2 for simulation
- Creating custom environments for specific robotic tasks
- Assessing performance metrics and training stability
Implementing RL in Robotics
- Developing control and motion policies
- Using RL for robotic manipulation tasks
- Multi-agent reinforcement learning for swarm robotics
Optimization, Deployment, and Real-World Application
- Tuning hyperparameters and shaping rewards
- Migrating learned policies from simulation to the real world (Sim2Real)
- Deploying trained models onto robotic hardware
Wrap-up 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 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.
