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---
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license: apache-2.0
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task_categories:
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- robotics
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- video-generation
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- world-models
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tags:
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- cosmos
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- nvidia
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- robot-learning
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- manipulation
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- multi-view
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size_categories:
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- n<1K
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---
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# Cosmos 2.5 Multi-View Robot Manipulation Dataset
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This dataset contains multi-view robot manipulation demonstrations formatted for training with NVIDIA Cosmos 2.5 world models.
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## Dataset Description
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- **Total Episodes**: 150
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- **Files per Episode**: 4 (3 videos + 1 caption file)
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- **Total Files**: 600
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- **Dataset Size**: ~1.1 GB
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- **Video Format**: MP4
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- **Caption Format**: JSONL
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## Dataset Structure
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```
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processeddata/
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└── input/
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├── episode_000001/
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│ ├── caption.jsonl
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│ ├── pinhole_base.mp4
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│ ├── pinhole_side.mp4
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│ └── pinhole_wrist.mp4
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├── episode_000002/
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│ ├── caption.jsonl
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│ ├── pinhole_base.mp4
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│ ├── pinhole_side.mp4
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│ └── pinhole_wrist.mp4
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...
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└── episode_000150/
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├── caption.jsonl
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├── pinhole_base.mp4
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├── pinhole_side.mp4
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└── pinhole_wrist.mp4
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```
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## File Descriptions
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### Video Files
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Each episode contains three synchronized video views:
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- **pinhole_base.mp4**: Base/overhead camera view
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- **pinhole_side.mp4**: Side camera view
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- **pinhole_wrist.mp4**: Wrist-mounted camera view
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### Caption Files
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Each `caption.jsonl` file contains three lines (one per view) with:
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- `caption`: Natural language description of the task
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- `view`: Camera view identifier
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- `tag`: Additional metadata (nullable)
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Example caption.jsonl:
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```json
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{"caption": "Pick up the bottle and place it into the blue box", "view": "pinhole_base", "tag": null}
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{"caption": "Pick up the bottle and place it into the blue box", "view": "pinhole_wrist", "tag": null}
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{"caption": "Pick up the bottle and place it into the blue box", "view": "pinhole_side", "tag": null}
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```
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## Usage
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### Loading the Dataset
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```python
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from datasets import load_dataset
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# Load the dataset
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dataset = load_dataset("JeffrinSam/cosmos2.5multip")
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# Access episode data
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episode_path = "processeddata/input/episode_000001"
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```
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### Using with Cosmos 2.5
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This dataset is formatted for training world models with NVIDIA Cosmos 2.5. Each episode provides:
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- Multi-view synchronized videos for spatial understanding
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- Natural language task descriptions
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- Structured format compatible with Cosmos data loaders
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## Applications
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- Robot manipulation learning
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- Multi-view world model training
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- Vision-language grounding for robotics
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- Physical AI simulation
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- Video prediction models
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## Citation
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If you use this dataset, please cite:
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```bibtex
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@misc{cosmos2.5multip,
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title={Cosmos 2.5 Multi-View Robot Manipulation Dataset},
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author={JeffrinSam},
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year={2025},
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publisher={HuggingFace},
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howpublished={\url{https://huggingface.co/datasets/JeffrinSam/cosmos2.5multip}}
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}
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```
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## License
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This dataset is released under the Apache 2.0 License.
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## Related Resources
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- [NVIDIA Cosmos](https://www.nvidia.com/en-us/ai/cosmos/)
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- [Physical AI Documentation](https://docs.nvidia.com/cosmos/)
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- [LeRobot Framework](https://github.com/huggingface/lerobot)
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