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YAML Metadata Warning: The task_categories "video-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata Warning: The task_categories "world-models" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Cosmos 2.5 Multi-View Robot Manipulation Dataset

This dataset contains multi-view robot manipulation demonstrations formatted for training with NVIDIA Cosmos 2.5 world models.

Dataset Structure

processeddata/
└── input/
    ├── episode_000001/
    │   ├── caption.jsonl
    │   ├── pinhole_base.mp4
    │   ├── pinhole_side.mp4
    │   └── pinhole_wrist.mp4
    ├── episode_000002/
    │   ├── caption.jsonl
    │   ├── pinhole_base.mp4
    │   ├── pinhole_side.mp4
    │   └── pinhole_wrist.mp4
    ...
    └── episode_000150/
        ├── caption.jsonl
        ├── pinhole_base.mp4
        ├── pinhole_side.mp4
        └── pinhole_wrist.mp4

File Descriptions

Video Files

Each episode contains three synchronized video views:

  • pinhole_base.mp4: Base/overhead camera view
  • pinhole_side.mp4: Side camera view
  • pinhole_wrist.mp4: Wrist-mounted camera view

Caption Files

Each caption.jsonl file contains three lines (one per view) with:

  • caption: Natural language description of the task
  • view: Camera view identifier
  • tag: Additional metadata (nullable)

Example caption.jsonl:

{"caption": "Pick up the bottle and place it into the blue box", "view": "pinhole_base", "tag": null}
{"caption": "Pick up the bottle and place it into the blue box", "view": "pinhole_wrist", "tag": null}
{"caption": "Pick up the bottle and place it into the blue box", "view": "pinhole_side", "tag": null}

Usage

Loading the Dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("JeffrinSam/cosmos2.5multip")

# Access episode data
episode_path = "processeddata/input/episode_000001"

Using with Cosmos 2.5

This dataset is formatted for training world models with NVIDIA Cosmos 2.5. Each episode provides:

  • Multi-view synchronized videos for spatial understanding
  • Natural language task descriptions
  • Structured format compatible with Cosmos data loaders

Applications

  • Robot manipulation learning
  • Multi-view world model training
  • Vision-language grounding for robotics
  • Physical AI simulation
  • Video prediction models

Citation

If you use this dataset, please cite:

@misc{cosmos2.5multip,
  title={Cosmos 2.5 Multi-View Robot Manipulation Dataset},
  author={JeffrinSam},
  year={2025},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/JeffrinSam/cosmos2.5multip}}
}

License

This dataset is released under the Apache 2.0 License.

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