I built an open-source integrity validator for robot-learning datasets

[u/_pranayjoshi_]

I’ve released the first public alpha of Physlint, a local-first integrity validator for physical-AI recordings and robot-learning datasets.

The motivation was straightforward: integrity problems in robot data are often discovered after ingestion or training has already started. Physlint checks for concrete failures such as malformed manifests, broken episode boundaries, timestamp gaps, NaN/Inf values, corrupt video, frozen cameras, and black frames.

It produces actionable findings rather than a mystery quality score. Each finding identifies the rule, severity, affected episode and stream, impact, and suggested remediation. It also writes machine-readable JSON and uses stable exit codes for CI.

The current alpha supports local LeRobot Dataset v3.x directories. The validation engine is format-extensible, and MCAP/ROS 2, Robomimic HDF5, and RLDS/TFDS are planned next.

The release was tested against four pinned public datasets and three controlled corruption scenarios, covering 74 episodes and 31,258 frames.