This is my collection of useful resources for learning robotics online. I will try my best to organize the materials (from generic to specific, fundamental to state-of-the-art) and hopefully update them frequently.

I will start with some online courses first (video lectures available).


MIT - Linear Algebra by Gilbert Strang is a good starting class.

Khan - Differential Equations

Harvard - Probability by Joe Blitzstein.

HMC - Real Analysis by Francis Su.

CUHK - Information Theory by Raymond W. Yeung.

Stanford - Convex Optimization by Stephen Boyd.

Harvard - Introduction to Computer Science by David J. Malan.

MIT - Introduction to Algorithms by Erik Demaine and Srini Devadas.

Upenn - Robotics by Vijay Kumar, CJ Taylor, Daniel E. Koditschek, Daniel Lee, Jianbo Shi, Kostas Daniilidis, Sid Deliwala.

Stanford - Machine Learning by Andrew Ng.

MIT - Artifical Intelligence by Patrick Henry Winston.


Stanford - Numpy by Justin Johnson.

Umich - Control with matlab by Bill Messner and Dawn Tilbury.

ETHZ - ROS by Péter Fankhauser, Dominic Jud, Martin Wermelinger and Marco Hutter.

Freiburg - C++ or Udacity - C++ or TheChernoProject - C++

Robot Mechanics

SNU - Robot Mechanics and Control by Frank C. Park (EiC of T-RO).

NU - Modern Robotics by Kevin Lynch (EiC of T-RO).

Dynamical Systems and Controls

Stanford - Linear Dynamical Systems by Stephen Boyd.

Shef - Modelling and Control by John Anthony Rossiter.

MIT - Nonlinear Systems by Jean-Jacques Slotine.

SNU - Nonlinear System Theory by Hyungbo Shim.

Motion Planning

NTU - Open-Source Robotics by Quang-Cuong Pham and Francisco Suárez-Ruiz.

Localization and Mapping

Freiburg - Robot Mapping

Optimal Control

Shef - Predictive control for beginners by John Anthony Rossiter.

MIT - Underactuated by Russ Tedrake, 2018.

Tsinghua - Dynamic Programming by Dimitri P. Bertsekas.

Artificial Intelligence

Cambridge - Information Theory, PR & NN by David MacKay.

MIT - Deep Learning for Self-Driving Cars by Lex Fridman, 2018.

Stanford - CNN for Visual Recognition by Fei-Fei Li and her students. - Deep Learning for Coders by Jeremy Howard and Rachel Thomas.

UCL - Reinforcement Learning by David Silver, 2015.

Berkeley - Deep Reinforcement Learning by Sergey Levine, 2018.

MIT - Artifical General Intelligence by Lex Fridman, 2018.

Berkeley - Deep RL Bootcamp by Pieter Abbeel et. al, 2017.