SIRLab-HGU/indy7-act-spatial-coverage

Indy7 ACT spatial-coverage dataset

880 episodes (176,837 frames) of a Neuromeka Indy7 arm with a Mand.ro Mark7 hand grasping a vertical PVC-U pipe, seen by a single fixed overhead RGB camera. The data backs a study of one question: does it matter more where demonstrations are collected, or how many there are?

• —Code, protocol and results: https://github.com/SIRLab-RobotArm/indy7-act-spatial-coverage
• —Trained policies: https://huggingface.co/SIRLab-HGU/indy7-act-spatial-coverage-models
• —Paper: not published yet; this card will be updated with the DOI

What is in it

• —880 episodes, 176,837 frames, 5 Hz, one task: grasp and lift the cylinder.
• —Episodes come from 220 human teleoperated recordings at 20 Hz. Each recording is split into four 5 Hz episodes by phase, so 220 x 4 = 880.
• —Recordings were made at nine marked table positions (G1-G9), 20 per position plus 40 extra at the centre.

Loading

from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("SIRLab-HGU/indy7-act-spatial-coverage")
print(dataset.meta.total_episodes, dataset.meta.total_frames)
sample = dataset[0]

The four training conditions

The study compares demonstration spread against demonstration count. The episode membership of each condition was frozen and checksummed before any model was trained; the manifests live in experiment/manifests/ in the code repository and refer to the episode indices used here.

How this file set was produced

The released videos are a re-encode of the image dataset the published policies were trained on, not a fresh conversion of the raw recordings. Episode indices, episode lengths, states, actions, timestamps and task indices are identical to the published artefacts; only the pixels differ, because PNG frames were encoded as AV1 video (libsvtav1, CRF 30, GOP 2, yuv420p) with LeRobot's own convert_image_to_video_dataset.

Measured difference over 500 randomly sampled frames:

Part of that difference is the RGB to yuv420p conversion, which is lossy even with a lossless codec. Policies in the accompanying release were trained on the PNG version; expect small differences if you retrain from these videos.

The raw 20 Hz HDF5 recordings (90 GB) are not published here. Ask the authors if you need them.

Collection setup

Limitations

• —One object, one camera pose, one lighting setup, one laboratory.
• —Demonstrations were collected position by position in blocks, so time of day, operator fatigue and lighting drift are entangled with position.
• —Only successful demonstrations were kept, so the data contains no failures.
• —Frames show the robot and the table; no people are intentionally recorded.

Citation

@software{kim_indy7_act_spatial_coverage_2026,
  author = {Kim, Yuyoung and Kim, Dogyung and Yoo, Jaehyeok and
            Kim, Dongyun and Kim, Dowook and Lee, WonHyong},
  title  = {Spatial coverage versus sample count in visuomotor imitation
            learning: an RGB-only ACT study on a real Indy7 arm},
  year   = {2026},
  url    = {https://github.com/SIRLab-RobotArm/indy7-act-spatial-coverage}
}

The paper citation will be added here once it is published.

License

Creative Commons Attribution 4.0 International (CC BY 4.0). You may share and adapt this dataset, including commercially, as long as you credit the authors and say whether you changed it. Full text: https://creativecommons.org/licenses/by/4.0/

The Mark7 hand geometry and the Neuromeka Indy7 driver are not part of this dataset. See the code repository's NOTICE.md for third-party components.