Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Introduction to Robot Learning
- Overview of machine learning applications in robotics
- Comparing supervised, unsupervised, and reinforcement learning
- RL applications in control, navigation, and manipulation
Fundamentals of Reinforcement Learning
- Markov decision processes (MDP)
- Understanding policies, value functions, and rewards
- Balancing exploration and exploitation
Classical RL Algorithms
- Q-learning and SARSA
- Monte Carlo and temporal difference methods
- Value and policy iteration
Deep Reinforcement Learning Techniques
- Integrating deep learning with RL (Deep Q-Networks)
- Policy gradient methods
- Advanced algorithms: A3C, DDPG, and PPO
Simulation Environments for Robot Learning
- Leveraging OpenAI Gym and ROS 2 for simulation
- Creating custom environments for specific robotic tasks
- Assessing performance and ensuring training stability
Applying RL to Robotics
- Developing control and motion policies
- Using reinforcement learning for robotic manipulation
- Multi-agent reinforcement learning in swarm robotics
Optimization, Deployment, and Real-World Integration
- Hyperparameter tuning and reward shaping
- Migrating learned policies from simulation to reality (Sim2Real)
- Deploying trained models on robotic hardware
Summary and Future Directions
Requirements
- Solid comprehension of machine learning concepts.
- Proficiency in Python programming.
- Working knowledge of robotics and control systems.
Target Audience
- Machine learning engineers.
- Robotics researchers.
- Developers engineering 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.
