Cursusaanbod

Introduction to Robot Learning

  • Overview of machine learning in robotics
  • Supervised vs unsupervised vs reinforcement learning
  • Applications of RL in control, navigation, and manipulation

Fundamentals of Reinforcement Learning

  • Markov decision processes (MDP)
  • Policy, value, and reward functions
  • Exploration vs exploitation trade-offs

Classical RL Algorithms

  • Q-learning and SARSA
  • Monte Carlo and temporal difference methods
  • Value iteration and policy iteration

Deep Reinforcement Learning Techniques

  • Combining deep learning with RL (Deep Q-Networks)
  • Policy gradient methods
  • Advanced algorithms: A3C, DDPG, and PPO

Simulation Environments for Robot Learning

  • Using OpenAI Gym and ROS 2 for simulation
  • Building custom environments for robotic tasks
  • Evaluating performance and training stability

Applying RL to Robotics

  • Learning control and motion policies
  • Reinforcement learning 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 reality (Sim2Real)
  • Deploying trained models on robotic hardware

Summary and Next Steps

Vereisten

  • An understanding of machine learning concepts
  • Experience with Python programming
  • Familiarity with robotics and control systems

Audience

  • Machine learning engineers
  • Robotics researchers
  • Developers building intelligent robotic systems
 21 Uren

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