---
license: mit
task_categories:
  - robotics
  - video-classification
tags:
  - minecraft
  - vla
  - vision-language-action
  - gaming
  - behavioral-cloning
size_categories:
  - 1M<n<10M
---

# Minecraft VLA Stage 1: Action Pretraining Data

Vision-Language-Action training data for Minecraft, processed from OpenAI's VPT contractor dataset.

## Dataset Description

This dataset contains frame-action pairs from Minecraft gameplay, designed for training VLA models following the [Lumine](https://www.lumine-ai.org/) methodology.

### Source
- **Original**: [OpenAI VPT Contractor Data](https://github.com/openai/Video-Pre-Training) (7.x subset)
- **Videos**: ~17,886 videos (~330 hours of early-game gameplay)
- **Task**: "Play Minecraft" with focus on first 30 minutes of new worlds

### Format

Each sample contains:
| Field | Type | Description |
|-------|------|-------------|
| `image` | bytes | 640x360 JPEG frame |
| `video_id` | string | Source video identifier |
| `frame_idx` | int | Frame number at 5Hz |
| `action` | string | Lumine-format action string |

### Action Format

```
<|action_start|> mouse_x mouse_y scroll ; K1 ; K2 ; K3 ; K4 <|action_end|>
```

- `mouse_x`, `mouse_y`: Mouse delta (-1000 to 1000)
- `scroll`: Hotbar scroll (always 0 - VPT uses number keys)
- `K1` to `K4`: Key combinations per 50ms chunk

**Example:**
```
<|action_start|> 45 -12 0 ; W ; W Space ; W LMB ; W LMB <|action_end|>
```

### Processing Details

- **Frame rate**: 5 FPS (downsampled from VPT's 20 FPS)
- **Action chunks**: 4 per frame (each 50ms = 200ms total)
- **Filtering**: Idle frames removed, loading screens filtered

## Usage

```python
from datasets import load_dataset

# Streaming (recommended - no download required)
ds = load_dataset("TESS-Computer/minecraft-vla-stage1", split="train", streaming=True)

for sample in ds:
    image = sample["image"]  # PIL Image or bytes
    action = sample["action"]
    # Process...
```

## Training Pipeline

This is Stage 1 of a 3-stage training pipeline:
1. **Stage 1** (this dataset): Action pretraining - learn observation→action mapping
2. **Stage 2**: Instruction following - add task instructions from JARVIS-VLA
3. **Stage 3**: Reasoning - add chain-of-thought before complex actions

## Citation

If you use this dataset, please cite:
- [OpenAI VPT](https://arxiv.org/abs/2206.11795) - Original contractor data
- [JARVIS-VLA](https://craftjarvis.github.io/JarvisVLA/) - Instruction annotations
- [Lumine](https://www.lumine-ai.org/) - Training methodology

## License

MIT License. Original VPT data is released under MIT by OpenAI.