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Duration 21 hours
Course Outline
Introduction to 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)
- Understanding policy, value, and reward functions
- Balancing exploration against exploitation
Traditional RL Algorithms
- Q-learning and SARSA methods
- Monte Carlo and temporal difference approaches
- Value iteration and policy iteration techniques
Deep Reinforcement Learning Methods
- Fusing deep learning with RL (Deep Q-Networks)
- Policy gradient methodologies
- Advanced algorithms: A3C, DDPG, and PPO
Simulation Platforms for Robot Learning
- Leveraging OpenAI Gym and ROS 2 for simulations
- Creating bespoke environments for specific robotic tasks
- Assessing performance metrics and training stability
RL Applications in Robotics
- Acquiring control and motion policies
- Utilizing RL for 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 physical reality (Sim2Real)
- Deploying trained models onto robotic hardware
Summary 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 constructing 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.
