Affordances from Human Videos as a Versatile Representation for Robotics
Paper • 2304.08488 • Published
This dataset is a TsFile conversion of the Hugging Face dataset
lerobot/cmu_stretch,
created using LeRobot.
The numeric robot state, action, reward, done, time, and task fields are stored in one TsFile table:
data/cmu_stretch.tsfilecmu_stretchTime, computed as round(timestamp * 1000) in millisecondsepisode_index, task_indexVector source columns are flattened into scalar TsFile FIELD columns:
observation.state -> observation_state_0 ... observation_state_3action -> action_0 ... action_7Scalar source columns are normalized as follows:
next.reward -> next_rewardnext.done -> next_doneindex -> sample_indexframe_index is kept unchangedThe source timestamp column is dropped because it is represented by
Time / 1000 seconds after conversion.
Camera videos are not included in this TsFile repository. Video data remains in the original Hugging Face dataset:
https://huggingface.co/datasets/lerobot/cmu_stretch/tree/main/videos
The converted meta/info.json keeps the original video path under
video_path_original and documents the conversion in tsfile_conversion.
@inproceedings{bahl2023affordances,
title={Affordances from Human Videos as a Versatile Representation for Robotics},
author={Bahl, Shikhar and Mendonca, Russell and Chen, Lili and Jain, Unnat and Pathak, Deepak},
booktitle={CVPR},
year={2023}
}
@article{mendonca2023structured,
title={Structured World Models from Human Videos},
author={Mendonca, Russell and Bahl, Shikhar and Pathak, Deepak},
journal={CoRL},
year={2023}
}