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Course Outline
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
Requirements
- 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 Hours
Testimonials (1)
its knowledge and utilization of AI for Robotics in the Future.
