README.md

---
license: cc-by-4.0
task_categories: [robotics]
tags: [physical-ai, imitation-learning, pixels, mujoco, in-browser, sim2sim]
---

# physicalai-bmi/forge-arm-pixels

**Real MuJoCo pixels** captured live from the Institute's in-browser Forge arm (WebGPU),
paired with the action the released state-checkpoint took. This is the exact training
set behind [`physicalai-bmi/nano-vla-pixels`](https://huggingface.co/physicalai-bmi/nano-vla-pixels).

- **2,500 frames** across **128 reaches**, `frames/f#####.png` (the rendered MuJoCo arm, 844×520).
- **`meta.json`** — per-frame `{ i, act:[3], obs:[7], reaches }`; `act` is the 3-D joint-delta action, `reaches` is the episode index (use it for an episode-level split).

## How it was made

Captured in a headless browser: the state checkpoint `forge-arm-reach-bc` drove the real
Forge MuJoCo arm on WebGPU, and each frame was grabbed by screenshotting the WebGPU canvas
(a page's own JS can't read WebGPU pixels; an external compositor screenshot can), synced to
the policy's action. Capture harness: `capture.cjs` in the Institute repo. CC-BY-4.0.

## Load (Python)

```python
import json, glob, cv2, numpy as np
meta = json.load(open("meta.json")); frames = sorted(glob.glob("frames/f*.png"))
X = np.stack([cv2.resize(cv2.imread(f), (48,48)) for f in frames])   # or full-res
Y = np.array([m["act"] for m in meta]); ep = np.array([m["reaches"] for m in meta])
```

Stacking 3 consecutive frames (velocity) lifts a pixel policy from 46.6% → 82.9% held-out
variance explained — see the model card.