Lectures

Full lecture recordings from the Stanford courses behind Principles of Robot Autonomy

For all other class materials — slides, homeworks, suggested readings, and schedules — see the course websites: Stanford AA174A and Stanford AA203.

Stanford AA203 Optimal and Learning-Based Control 19 lectures · Watch now ↓

Stanford AA174A / AA274A Principles of Robot Autonomy I Coming soon!

Stanford AA203 Optimal and Learning-Based Control

19 lecture recordings · course website

Lecture 1 Course Overview and Intro to Nonlinear Optimization

Lecture 2 Optimization Theory

Lecture 3 Calculus of Variations

Lecture 4 Indirect Methods for Optimal Control

Lecture 5 Pontryagin’s Minimum Principle and Computational Methods

Lecture 6 Direct Methods for Optimal Control

Lecture 7 Dynamic Programming and Discrete-Time LQR

Lecture 8 Nonlinearity: Tracking LQR, Iterative LQR, Differential Dynamic Programming

Lecture 9 Stochastic Dynamic Programming, Value Iteration, and Policy Iteration

Lecture 10 Hamilton–Jacobi–Bellman, Hamilton–Jacobi–Isaacs, and Reachability Analysis

Lecture 11 Introduction to Model Predictive Control

Lecture 12 Persistent Feasibility of MPC, Stability, and Explicit MPC

Lecture 13 Intro to Learning, System Identification and Adaptive Control

Lecture 14 Intro to Imitation Learning and Reinforcement Learning

Lecture 15 Imitation Learning

Lecture 16 Fundamentals of Reinforcement Learning

Lecture 17 Model-free Reinforcement Learning: Value-based Methods

Lecture 18 Model-free Reinforcement Learning: Policy Optimization

Lecture 19 Model-based Reinforcement Learning and Conclusions