Datasets:
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observation.state list | action list | timestamp float32 0 29.8 ⌀ | frame_index int64 0 895 ⌀ | episode_index int64 0 282 ⌀ | index int64 0 98.3k ⌀ | task_index int64 0 0 ⌀ |
|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
Flip bagel
Visualizers
Overview
This dataset is a conversion from an upstream robotics dataset into LeRobot v3-compatible format. It is intended to provide reproducible access in a unified schema.
Source Dataset
- Dataset ID:
hub-flip-bagel - Project:
unknown - Task:
Flip bagel - Upstream download/source URL: https://real.stanford.edu/maniwav/data/flip/replay_buffer.zarr.zip
- Upstream project page: https://umi-data.github.io/
- Contact: liuzeyi@stanford.edu
- Original paper: Data Scaling Laws in Imitation Learning for Robotic Manipulation (2024)
- Notes: 283 demos; 1 envs; obs=Image,Proprio,Audio; actions=6 DoF End Effector,Parallel Gripper
Dataset Format
- Storage format: LeRobot v3 parquet-based dataset
- Total episodes: 283
- Total frames: 98335
- FPS: 30
- Robot type: generic_robot
- Observation feature:
observation.statewith shape[7] - Action feature:
actionwith shape[7] - Video streams: present
Video Modalities
observation.images.camera0_rgb: shape[224, 224, 3]- Frame rate:
30FPS
Conversion Notes
- Target schema: LeRobot v3
- Conversion path: ingest -> preprocess -> zarr probe -> convert 1 episode -> upload
actionfallback may be used when upstreamdata/actionis missing.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "generic_robot",
"total_episodes": 283,
"total_frames": 98335,
"fps": 30,
"splits": {"train": "0:283"},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"features": {
"observation.state": {"dtype": "float32", "shape": [7]},
"action": {"dtype": "float32", "shape": [7]}
}
}
Data Splits
{'train': '0:283'}
Citation
This dataset was created using LeRobot. Please also cite the original upstream project and paper listed above.
Attribution
- Original dataset credit remains with upstream creators.
- Conversion and curation contribution:
- Convertor: David Yuan, davidy02@stanford.edu, Stanford University
- NMotion Initiative (opensource community): we make embodied data accessible
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