README.md

---
license: mit
task_categories:
- reinforcement-learning
pretty_name: pan-2 VPT (64px episodes + packed shards)
size_categories:
- 100GB<n<1TB
tags:
- minecraft
- vpt
- openai
- numpy
---

# pan-2 VPT

Cleaned OpenAI VPT contractor demos, packed for [Infatoshi/pan-2](https://github.com/Infatoshi/pan-2).

This is **not** a re-dump of the original VPT Azure blobs, and it is **not** the same layout as `zhwang4ai/OpenAI-Minecraft-Contractor` (jsonl-only), `p-doom/openai-minecraft-dataset` (Grain/ArrayRecord tars), or `TESS-Computer/minecraft-vla-stage1` (parquet JPEGs at 640x360 / 5 Hz).

| | |
|---|---|
| Episodes | 1625 validated stems (27 dropped) |
| Frames | 8,155,382 (~113.3 h at 20 Hz) |
| Image | `uint8 [T, 64, 64, 3]` |
| Actions | `float32 [T, 25]` (23 buttons + camera dx/dy in `[-1, 1]`) |
| Raw video | H.264 640x360 @ 20 fps, sibling jsonl |

## Layout

```
raw/<stem>.mp4
raw/<stem>.jsonl          # original VPT per-tick action dicts
episodes/<stem>.img.npy   # (T, 64, 64, 3) uint8
episodes/<stem>.act.npy   # (T, 25) float32
shards/manifest.jsonl
shards/shard-XXXXX.frames.npy
shards/shard-XXXXX.act.npy
meta/README.md
meta/cleanup_summary.json
meta/episodes_manifest.jsonl
```

Episodes never straddle shards. `manifest.jsonl` starts with a header:

```json
{"type":"header","version":1,"image_size":64,"act_dim":25,"n_shards":24,"total_frames":8155382,"total_episodes":1625}
```

then one `segment` row per episode (`shard`, `stem`, `offset`, `n_frames`, `has_act`).

## Load shards

```python
import json
from pathlib import Path
import numpy as np

root = Path("shards")
header = json.loads(root.joinpath("manifest.jsonl").read_text().splitlines()[0])
frames = np.load(root / "shard-00000.frames.npy", mmap_mode="r")  # (N, 64, 64, 3) uint8
acts = np.load(root / "shard-00000.act.npy", mmap_mode="r")        # (N, 25) float32
```

Rebuild shards from episodes with the pan-2 repo:

```bash
uv run python scripts/build_shards.py --source episodes --episodes-dir episodes --out shards
```

## Source

OpenAI Video PreTraining contractor data (Baker et al., 2022). Original index/blobs: `https://openaipublic.blob.core.windows.net/`. Some origin mp4s 404; this tree is the surviving cleaned 1625-stem subset used for pan-2.

Action columns: see `src/pan2/actions.py` in the code repo.

## License

MIT, same as the upstream VPT contractor release. Cite OpenAI VPT if you use this.