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Duration 21 hours
Course Outline
Foundations of Robot Learning
- The role of machine learning in robotics
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Practical applications of RL in control, navigation, and manipulation
Core Concepts in Reinforcement Learning
- Markov decision processes (MDP)
- Defining policies, value functions, and reward structures
- Managing the trade-off between exploration and exploitation
Traditional RL Algorithms
- Q-learning and SARSA methodologies
- Monte Carlo and temporal difference learning methods
- Value iteration and policy iteration strategies
Advanced Deep Reinforcement Learning
- Fusing deep learning with RL via Deep Q-Networks
- Understanding policy gradient methods
- Exploring advanced algorithms including A3C, DDPG, and PPO
Robot Learning Simulation Environments
- Leveraging OpenAI Gym and ROS 2 for simulation
- Creating custom environments tailored to specific robotic tasks
- Assessing model performance and training stability
Implementing RL in Robotic Systems
- Developing control and motion policies
- Applying RL to enhance robotic manipulation
- Utilizing multi-agent RL in swarm robotics
Optimization, Deployment, and Real-World Application
- Tuning hyperparameters and shaping rewards
- Transitioning learned policies from simulation to physical reality (Sim2Real)
- Deploying trained models onto physical robotic hardware
Conclusion and Future Pathways
Requirements
- A solid grasp of fundamental machine learning concepts
- Proficiency in Python programming
- Basic knowledge of robotics and control systems
Target Audience
- Machine learning engineers
- Robotics researchers
- Developers focused on 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.
