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--- |
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license: mit |
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task_categories: |
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- robotics |
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- video-classification |
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tags: |
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- minecraft |
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- vla |
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- vision-language-action |
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- gaming |
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- behavioral-cloning |
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size_categories: |
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- 1M<n<10M |
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--- |
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# Minecraft VLA Stage 1: Action Pretraining Data |
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Vision-Language-Action training data for Minecraft, processed from OpenAI's VPT contractor dataset. |
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## Dataset Description |
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This dataset contains frame-action pairs from Minecraft gameplay, designed for training VLA models following the [Lumine](https://www.lumine-ai.org/) methodology. |
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### Source |
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- **Original**: [OpenAI VPT Contractor Data](https://github.com/openai/Video-Pre-Training) (7.x subset) |
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- **Videos**: ~17,886 videos (~330 hours of early-game gameplay) |
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- **Task**: "Play Minecraft" with focus on first 30 minutes of new worlds |
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### Format |
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Each sample contains: |
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| Field | Type | Description | |
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|-------|------|-------------| |
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| `image` | bytes | 640x360 JPEG frame | |
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| `video_id` | string | Source video identifier | |
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| `frame_idx` | int | Frame number at 5Hz | |
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| `action` | string | Lumine-format action string | |
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### Action Format |
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``` |
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<|action_start|> mouse_x mouse_y scroll ; K1 ; K2 ; K3 ; K4 <|action_end|> |
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``` |
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- `mouse_x`, `mouse_y`: Mouse delta (-1000 to 1000) |
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- `scroll`: Hotbar scroll (always 0 - VPT uses number keys) |
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- `K1` to `K4`: Key combinations per 50ms chunk |
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**Example:** |
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``` |
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<|action_start|> 45 -12 0 ; W ; W Space ; W LMB ; W LMB <|action_end|> |
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``` |
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### Processing Details |
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- **Frame rate**: 5 FPS (downsampled from VPT's 20 FPS) |
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- **Action chunks**: 4 per frame (each 50ms = 200ms total) |
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- **Filtering**: Idle frames removed, loading screens filtered |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Streaming (recommended - no download required) |
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ds = load_dataset("TESS-Computer/minecraft-vla-stage1", split="train", streaming=True) |
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for sample in ds: |
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image = sample["image"] # PIL Image or bytes |
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action = sample["action"] |
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# Process... |
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``` |
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## Training Pipeline |
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This is Stage 1 of a 3-stage training pipeline: |
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1. **Stage 1** (this dataset): Action pretraining - learn observation→action mapping |
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2. **Stage 2**: Instruction following - add task instructions from JARVIS-VLA |
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3. **Stage 3**: Reasoning - add chain-of-thought before complex actions |
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## Citation |
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If you use this dataset, please cite: |
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- [OpenAI VPT](https://arxiv.org/abs/2206.11795) - Original contractor data |
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- [JARVIS-VLA](https://craftjarvis.github.io/JarvisVLA/) - Instruction annotations |
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- [Lumine](https://www.lumine-ai.org/) - Training methodology |
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## License |
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MIT License. Original VPT data is released under MIT by OpenAI. |
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