Control for Robotics
From Optimal Control to Reinforcement Learning
Bridging the gap between classical control theory and modern AI, Control for Robotics offers a comprehensive journey from the foundations of Optimal Control to modern Deep Reinforcement Learning. We have designed this course to combine rigorous derivations with practical programming exercises to help you build controllers that can handle the complexity of real-world robot decision-making. Whether you are a student or a practitioner, these resources will empower you to design intelligent, adaptive robotic systems.
The course is structured into eight main chapters:
- Introduction to Optimal Control
- Linear Quadratic Optimal Control
- Optimization Fundamentals
- Iterative Optimal Control Algorithms
- Model Predictive Control
- Model Learning and Learning-Based Control
- Introduction to Reinforcement Learning
- Deep Reinforcement Learning
