Principles of Robot Autonomy

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 17 lectures · Watch now ↓

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

Stanford AA174A / AA274A Principles of Robot Autonomy I

17 lecture recordings · course website

Lecture 1 Course Overview and Mobile Robot Kinematics Lecture 2 The Robot Operating System (ROS) Lecture 3 Trajectory Optimization Lecture 4 Trajectory Tracking Lecture 5 Advanced Trajectory Optimization Lecture 6 Motion Planning: Graph Search Methods Lecture 7 Motion Planning: Sampling-Based Methods Lecture 8 Sensors and Introduction to Computer Vision Lecture 9 Camera Models & Calibration Lecture 10 Stereo Vision Lecture 11 Image Processing Lecture 12 Classic Visual Recognition Lecture 13 Localization & Filters Lecture 14 Parametric Filtering Lecture 15 EKF Localization Lecture 16 EKF SLAM Lecture 17 Multi-Sensor Fusion