Find the right datasets and models, validate robot data before it reaches training, and compile it for the next run.
Selected for source evidence, usability, adoption, freshness, and distinct value. Selection readiness measures catalog evidence, not task performance. Confidence describes the supporting evidence.
How readiness scores work3 selected from 364
Datasets + models
modelHugging Face LeRobot
Readiness 76/100 · 63% confidence
Compact open vision-language-action policy designed for practical robot fine-tuning and deployment through the LeRobot ecosystem.
modelNVIDIA
Readiness 76/100 · 63% confidence
Open foundation model for generalist humanoid robots, focused on whole-body and manipulation behavior from multimodal robot data.
datasetHugging Face · ropedia-ai/xperience-10m
Readiness 77/100 · 48% confidence
A large egocentric multimodal dataset with synchronized video streams, audio, depth, poses, mocap, IMU, and hierarchical language annotations.
Each step narrows uncertainty and leaves a result the team can review.
Open the official repository, paper, or project page.
Compare access, license, modalities, artifacts, and adoption.
Audit the exact dataset revision with openbot-data.
Use, defer, repair, or reject—with the reason preserved.
openbot-data
Inspect a robot-video or LeRobot dataset locally. Nothing is uploaded, and every result stays reviewable before the next run.
Run your first audit
pip install 'openbot-data[lerobot]'
openbot-data audit \
--format lerobot \
--integrity full \
--out ./audit.json \
--fail-on error \
-- './lerobot_dataset'
openbot-data readiness \
--profile lerobot-core \
--integrity full \
--out ./readiness.json \
-- './lerobot_dataset'Replace ./lerobot_dataset with your local folder. The first-audit guide explains the reports and next checks.