The library
Courses
12 curated free Stanford and MIT lecture series, from linear algebra to deep reinforcement learning.
38 resources
Stephen Boyd, Stanford
54 short lectures, ~18.5 h. Linear algebra as a tool, not proofs.
CoursesM1Oussama Khatib, Stanford
16 lectures, ~19 h. Kinematics, the Jacobian, dynamics, control.
CoursesM2Fei-Fei Li and team, Stanford
18 lectures, ~21 h. Ends on robot learning.
CoursesM3Stanford
IMUs, lidar, cameras, and why real sensors drift.
CoursesM3Marco Pavone and Daniele Gammelli, Stanford
19 lectures, Spring 2026. LQR, MPC, then RL.
CoursesM4Chelsea Finn, Stanford
18 lectures, ~20 h. Lecture 16 is RL for real robots.
CoursesM5Emma Brunskill, Stanford
Slower and more mathematical, if CS224R moves too fast.
CoursesM5Andrej Karpathy
10 videos, ~19 h. From one neuron to GPT-2, every line typed live.
CoursesM3Sergey Levine, UC Berkeley
25 lectures + homework code, ~36 h. Spring 2026.
CoursesM5Russ Tedrake, MIT
23 lectures, ~30 h. Grasping, planning, behavior cloning. Exercises on Drake.
CoursesM6Russ Tedrake, MIT
21 lectures, ~28 h. The math behind robots that walk, run and flip.
CoursesM4Stanford ASL
New talks every quarter. One a week keeps you current.
CoursesM6MIT / Toyota Research
Behind the MIT manipulation and underactuated courses.
Simulators