you balanced your robot dataset by embodiment. the embeddings say you balanced it by recording session i pulled 10 episodes from each of the 50 robot types in lerobot's community dataset and embedded every clip with qwen3-vl 1. 495 of 497 clips have their nearest neighbor in the same session 2. 292 score below 0.1 uniqueness 3. the two "most unique" clips are single-frame recordings, 0.02 seconds long filter the session out and the cross-embodiment signal shows up: a cloth fold on one rig lands next to so100 arms folding cloth i indexed all 497 episodes in fiftyone so you can search by text, cut the near-duplicates, and export a curated lerobot v3 subset one prompt to an agent with fiftyone skills built the whole thing start here, read the full blog: read the dataset card: @LeRobotHF @Alibaba_Qwen