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.
Course Outline
Introduction to Robot Learning
- Broad overview of machine learning applications in robotics
- Comparing supervised, unsupervised, and reinforcement learning
- Use cases of RL in control, navigation, and manipulation
Core Principles of Reinforcement Learning
- Markov decision processes (MDP)
- Defining policy, value, and reward functions
- Balancing exploration and exploitation
Traditional RL Algorithms
- Q-learning and SARSA
- Monte Carlo and temporal difference methods
- Value iteration and policy iteration
Advanced Deep Reinforcement Learning Methods
- Merging deep learning with RL (Deep Q-Networks)
- Policy gradient approaches
- Modern algorithms: A3C, DDPG, and PPO
Simulation Environments for Robot Training
- Leveraging OpenAI Gym and ROS 2 for simulation
- Creating bespoke environments for specific robotic tasks
- Assessing performance metrics and training stability
Application of RL in Robotics
- Developing control and motion policies
- Utilizing RL for robotic manipulation tasks
- Applying multi-agent RL in swarm robotics
Optimization, Deployment, and Real-World Implementation
- Hyperparameter tuning and reward shaping
- Transferring learned policies from simulation to the real world (Sim2Real)
- Deploying trained models onto robotic hardware
Conclusion and Future Directions
Requirements
- A solid grasp of core machine learning concepts
- Proficiency in Python programming
- Knowledge of robotics and control systems
Target Audience
- Machine learning engineers
- Robotics researchers
- Developers focused on creating intelligent robotic systems
21 Hours
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.