Biscue5/pi0-egoscaler-v2
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observation.state list | action list | original.index int64 | timestamp float32 | frame_index int64 | episode_index int64 | index int64 | task_index int64 |
|---|---|---|---|---|---|---|---|
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] | 11 | 1.4 | 28 | 3 | 98 | 3 |
[
0.5105050802230835,
0.5769522190093994,
1.495235562324524,
0.9006025195121765,
0.16355516016483307,
-0.24785961210727692,
0.9543437957763672,
-0.3570444583892822,
-0.2499551624059677
] | [
0.0027880175039172173,
0.0005757134058512747,
-0.0035054211039096117,
0.9997623562812805,
0.02172882854938507,
-0.021719124168157578,
0.9997496008872986,
-0.0018652617000043392,
0.005350460298359394
] | 11 | 1.45 | 29 | 3 | 99 | 3 |
End of preview.
EgoScalerV2 Dataset
This dataset accompanies our work on Developing Vision-Language-Action Model from Egocentric Videos. It provides 6DoF object trajectories paired with egocentric visual observations and natural-language action descriptions, formatted in the LeRobot v2.0 schema so it can be consumed directly by LeRobot-compatible pipelines.
- 🌐 Project page: https://biscue5.github.io/egovla-project-page/
- 📄 Paper: Developing Vision-Language-Action Model from Egocentric Videos (arXiv:2509.21986)
- 🧰 Format: LeRobot v2.0 (Parquet + MP4)
- 🪪 License: Apache-2.0
Dataset Structure
{
"codebase_version": "v2.0",
"robot_type": "human activity",
"total_episodes": 45157,
"total_frames": 1409418,
"total_tasks": 30214,
"total_videos": 45157,
"total_chunks": 46,
"chunks_size": 1000,
"fps": 20,
"splits": {
"train": "0:45157"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"observation.images.cam_high": {
"dtype": "video",
"shape": [
3,
224,
224
],
"names": [
"channel",
"height",
"width"
],
"info": {
"video.fps": 20.0,
"video.height": 224,
"video.width": 224,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.state": {
"dtype": "float32",
"shape": [
9
],
"names": {
"axes": [
"x",
"y",
"z",
"r00",
"r10",
"r20",
"r01",
"r11",
"r21"
]
}
},
"action": {
"dtype": "float32",
"shape": [
9
],
"names": {
"axes": [
"x",
"y",
"z",
"r00",
"r10",
"r20",
"r01",
"r11",
"r21"
]
}
},
"original.index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
Citation
If you use this dataset, please cite:
@article{yoshida2025developing,
title = {Developing Vision-Language-Action Model from Egocentric Videos},
author = {Yoshida, Tomoya and Kurita, Shuhei and Nishimura, Taichi and Mori, Shinsuke},
journal = {arXiv preprint arXiv:2509.21986},
year = {2025}
}
The data construction pipeline builds on:
@InProceedings{Yoshida_2025_CVPR,
author = {Yoshida, Tomoya and Kurita, Shuhei and Nishimura, Taichi and Mori, Shinsuke},
title = {Generating 6DoF Object Manipulation Trajectories from Action Description in Egocentric Vision},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2025},
pages = {17370-17382}
}
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Models trained or fine-tuned on Biscue5/egoscaler-v2