Interactive textbook · embodied AI, from scratch
Read robotics like a book.Run every page like a lab.
Minimal, real, readable code — one runnable file per chapter, from a bare simulation loop to a policy you train, export, and drive in your browser. No framework to excavate, no black boxes. The code is the product, and it all runs on a free Colab T4 or a CPU laptop.
Nothing completed yet — read a chapter, or commit a prediction in an exercise, and it shows up here. No account needed; this lives only in your browser.
- Phase 0 · Foundations0/6 · 0%
- Phase 1 · Imitation0/9 · 0%
- Phase 2 · Reinforcement0/8 · 0%
- Phase 3 · Advanced0/9 · 0%
- Phase 4 · Capstone0/3 · 0%
- Phase 5 · Practitioner0/8 · 0%
See it work
live · P2This is the real behavior-cloning policy from Chapter 1.1, running in your
browser on a MuJoCo-WASM simulation — the same bc.py you'll
train. Drag the block out of the region the demonstrations covered, and watch
a confident policy fail. That failure has a name, and the whole course is
built around understanding it.
Why this course is built the way it is
the code is the product
One file you can read top to bottom
Every chapter builds a single runnable script — a few hundred lines, no framework to excavate. You read it like a textbook, and every line you read is a line that runs.
runs on the free tier
A laptop or a free Colab T4 is enough
Every learner-facing path completes on CPU or a free T4. The wall-clock times on each page are measured on real hardware, never estimated; where a tier hasn't been measured yet, the page says so.
honest by construction
Real sims, real policies, real numbers
The browser demos run the same code you train locally. Seeds are mandatory, results reproduce within a recorded band, and every number you read traces back to a run — including the ones that show a method failing.
The arc
Six phases, one runnable file at a time.
43chapters are live and readable today — the whole arc from a bare simulation loop to a from-scratch practitioner's stack and a real-arm graduation. The only thing not yet linkable is the leaderboard capstone: it's graded against hidden seeds, so it opens when the grading server ships, and not before.
Phase 0 · Foundations
6 liveMake the simulator behave. Step physics by hand, author a scene, get frames and rotations right, and record your first teleoperated dataset.
Phase 1 · Imitation
9 liveTeach a policy from demonstrations. Behavior cloning, exactly why it breaks, and the models built to stop it from breaking.
- 1.1Behavior CloningThe Dumbest Thing That Works
- 1.2Data Is the Policy
- 1.3ACTCommit to the Chunk
- 1.4Generative Policies IDiffusion
- 1.5Generative Policies IIFlow Matching
- 1.6Evaluation Is Hard
- 1.7Tokens Meet TorquesThe Tiny VLA, Part I (the data)
- 1.8The Tiny VLA IITrain It
- 1.9Graduation Bridge ILeRobot for Real
Phase 2 · Reinforcement
8 liveLearn from reward instead of examples — for when demonstrations run out and the robot has to try, fail, and improve.
- 2.1PPOThe Policy That Acts and Sees the Consequences
- 2.2SAC and the Off-Policy Bargain
- 2.34096 Robots at OncePPO on MJX
- 2.4Reward Design Is Programming
- 2.5LocomotionThe Quadruped Walks
- 2.6Sim-to-Real Intuition Lab ILatency & Noise
- 2.7Sim-to-Real Intuition Lab IIRandomize to Generalize
- 2.8Concepts of ROS, Without ROSa Pub-Sub Control Runtime
Phase 3 · Depth (optional)
9 liveOff the main line, taken when the itch strikes. Learn a world model, build a physics engine from scratch — dynamics, constraints, contact — compare it against the simulator you have trusted all along, then plan through your own engine with sampling-based MPC. The capstone needs none of it.
- 3.1World Models ILearning the Simulator
- 3.2World Models IIActing in Imagination
- 3.3Build a Physics Engine IUnconstrained Dynamics
- 3.4Build a Physics Engine IIJoints & Constraints
- 3.5Build a Physics Engine IIIContact
- 3.6Full CircleRun Your ch1.1 Policy in the Engine You Built
- 3.7Datasets at Scale
- 3.8Reading the Frontier
- 3.9Plan Through Your EngineSampling-Based MPC (CEM / MPPI)
Phase 4 · Capstone
3 livecapstone gatedPut every phase together into one project you can point at, measure honestly, and show — including the human-in-the-loop RL that turns a decent policy into a reliable one.
- 4Offline RL PrimerBeat the Data With Its Own Reward
- 4.2CorrectionsHuman-in-the-Loop Data
- 4.3RL Post-TrainingHIL-SERL in Sim
coming · gatedThe leaderboard capstone is scored against hidden seeds, so those chapters open only once the grading server ships — they aren't linkable yet, and we won't pretend they are. The chapters that lead into them are already here.Phase 5 · Practitioner
8 liveBuild the practitioner's stack from scratch. A ViT and contrastive vision-language for perception, the two-tower VLA shape and a FAST action tokenizer, LoRA and INT8 quantization by hand — then the real-arm teleop → record → train → deploy loop that graduates you off the simulator.
- 5.1Patches & AttentionA ViT From Scratch
- 5.2Why AlignedContrastive Vision-Language Pretraining
- 5.3Control From PixelsVisuomotor Behavior Cloning
- 5.4The Production VLA ShapePrefix, Suffix, and the Action Expert
- 5.5FASTTurning Torques into Tokens (DCT -> Quantize -> BPE)
- 5.6LoRA From ScratchAdapt a Frozen Policy
- 5.7Quantize a Policy by HandINT8 Is a Scale, Not a Rounding
- 5.8The Real LoopTeleoperate, Record, Train, Deploy — on a Real Arm's Body (in sim)