Course Outline
Introduction to Robot Learning
- Overview of machine learning applications in robotics
- Distinguishing between supervised, unsupervised, and reinforcement learning
- RL applications in control, navigation, and manipulation
Fundamentals of Reinforcement Learning
- Markov decision processes (MDP)
- Defining policy, value, and reward functions
- Balancing exploration and exploitation trade-offs
Classical RL Algorithms
- Q-learning and SARSA methods
- Monte Carlo and temporal difference techniques
- Value iteration 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 training stability
Applying RL to Robotics
- Developing control and motion policies
- Utilizing RL for robotic manipulation
- Multi-agent reinforcement learning in swarm robotics
Optimization, Deployment, and Real-World Integration
- Hyperparameter tuning and reward shaping
- Transferring learned policies from simulation to real-world scenarios (Sim2Real)
- Deploying trained models on physical robotic hardware
Summary and Next Steps
Requirements
- A solid understanding of core machine learning concepts
- Proficiency in Python programming
- Familiarity with robotics principles and control systems
Target Audience
- Machine learning engineers
- Robotics researchers
- Developers engineering intelligent robotic systems
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3900 € + VAT*
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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.
