Run our released checkpoints on your device.
Open should mean the weights, the data, and a way to actually run them. The first is a small, on-device control policy trained on our own openly published dataset; below it, a quadruped gait discovered from scratch by an on-device learner. Both replay live on this page, on the same simulated robots they learned on. No server. No cloud.
And a learned gait, walking on your device.
The same idea, one level up: not a controller trained from a dataset, but a behavior discovered from scratch. This is a quadruped walking gait our on-device learner found by Evolution Strategies, no gradients, no cloud, about a minute of search. What ships is the winning genome: ten numbers that shape a central pattern generator. Below, that exact released genome replays the same forward-walk rollout that scored it, live on the same simulated quadruped it learned on.
Toggle between the two gaits: the learned genome consistently covers about 37% more ground in the same 3.2-second walk than the hand-tuned baseline, a margin that is reproducible, because the physics is deterministic and the search is seeded. The distance you see is measured live on your device; the figures published on the model card (1.42 m versus 1.04 m) came from the same rollout loop and land within a few percent. Found with antithetic Evolution Strategies (λ=16, 28 generations) over a headless MuJoCo-WASM forward-walk rollout, no gradients, no cloud.
Weights, data, and a way to run them.
A released artifact is only as open as your ability to reproduce and run it. This checkpoint ships all three, together.
Released, not just described
The trained parameters ship under CC-BY-4.0 as safetensors, the artifact itself, downloadable and reusable, not a figure in a paper.
The set it learned from
Trained on our own openly published dataset, generated with no hardware and no cloud. Anyone can regenerate it in about a minute.
On your device, right now
The same weights close the loop on the same simulated arm, live on this page, no server, no cloud. Open you can actually execute.
Small, and measured.
Evaluated on two episodes held out entirely from training (an episode-level split, not a frame-level one) so the score reflects generalization, not memorization.
Held-out action MSE 5.68×10⁻⁷ against a predict-the-mean baseline of 4.20×10⁻⁴, with train and validation error essentially equal (no meaningful overfitting). The full metrics, training recipe, and citation are in the model card.
The same weights, everywhere.
The JavaScript path uses plain float32 arrays and is verified bit-for-bit equivalent to the safetensors forward pass.
No server, plain arrays
const m = await (await fetch('policy.web.json')).json();
const silu = x => x/(1+Math.exp(-x));
// z-score in, MLP 7→128→128→3, z-score out
function act(obs){ /* 3 matmuls */ return dq; }NumPy + safetensors
from safetensors.numpy import load_file
w = load_file('model.safetensors')
def act(obs):
h = silu(w['0.weight'] @ ((obs-xm)/xs) + w['0.bias'])
... # 3 layers → denormalizeThe loop you're watching
// the released weights ARE the controller driving
// the arm in the panel: obs(7) → policy → action(3),
// stepped through MuJoCo at ~60 Hz.What it is, and what it isn't.
We release small honest artifacts because that is how open science compounds. Here is exactly what this one covers.
One task, in simulation
It solves a single reach task on a simulated arm. It is an educational reference policy, not a foundation model and not safety-tested for any physical deployment.
A small demonstration set
Eleven episodes from a scripted expert. The near-perfect fit reflects a low-dimensional, clean task; the point is a released, reproducible, runnable artifact.
Sim-to-real needs care
Because it is simulation-generated, transferring to hardware needs the usual domain-gap work. The dataset and training are open so you can extend both.
Weights, dataset, and the maker.
forge-arm-reach
The openly published trajectories it learned from, 11 episodes, generated on-device.
forge-quadruped-gait
The released 10-parameter CPG genome (gait.json), found on-device by Evolution Strategies, with the method and numbers in the card.
Forge
Record a new dataset and train a policy, or watch a gait learn to walk, the same loops that produced these.