Posting here because this is directly about Hub data quality.
LeRobotDataset is now the largest dataset category on the Hub. I built trajlens, an open-source linter for it, and ran it against 100 random public lerobot-tagged datasets to measure real-world quality.
Results: 19 PASS, 13 FAIL, 47 load errors (mostly the known v2.x streaming limitation, not trajlens bugs), 21 timeout.
Of the datasets that linted successfully, two named upstream bugs account for most of the failures:
lerobot issue 2401 (v2.1 to v3.0 episode-frame corruption): roughly 19% prevalence
lerobot issue 3177 (timestamp float drift): roughly 3% prevalence
If you maintain a dataset on the Hub, running trajlens lint your-org/your-dataset takes under a minute and will tell you if either of these applies to your data.
pip install trajlens
Repo: GitHub - Kunal-Somani/trajlens: The quality and synthesis layer for the open robot-learning data ecosystem · GitHub
Apache 2.0, pluggable check registry, contributions welcome.
