Six Questions to Ask a LeRobot Dataset Before You Train

Before training robot policies or vision-language-action models on LeRobot datasets, the article recommends systematically reviewing episodes for structural integrity, motion quality, idle time, command-to-state tracking, gripper behavior, peer consistency, camera reliability, and instruction quality rather than relying on a small manual sample. It presents a FiftyOne plugin for local LeRobot v3 datasets that computes mostly lightweight, heuristic metrics from actions, states, metadata, and optionally decoded video, then ranks episodes within canonicalized task groups so users can inspect likely problems such as broken episode pointers, frozen cameras, action-video lag, stalls, dead frames, generic task labels, or inconsistent demonstrations. The approach avoids automatic deletion: integrity and language issues receive separate verdicts, correlated metrics are consolidated into groups, and scores are intended to prioritize a human review queue, with users applying tags such as review or exclude-candidate before exporting curated data. The article emphasizes limitations in the evidence behind many curation metrics, including confounding by episode length and the possibility that unusual or imperfect demonstrations may still improve policy performance, and advises validating any filtering decision by comparing models trained on curated and full datasets.

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