What Matters When Cotraining Robot Manipulation Policies on Everyday Human Videos?
Human video datasets used for cotraining robot manipulation policies largely consist of curated demonstrations where motions are orchestrated to resemble robot behavior and 3D hand poses are captured with specialized hardware. A more plentiful source of data is everyday Internet video, but it is an open question what factors enable transfer from such videos to robots. We investigate this using a new dataset of 532 human videos with 28 hours of high-quality triangulated hand labels and natural motions. We find that hand pose quality affects transfer, but even with accurate hands, the inherent motion gap hinders transfer unless the vision and policy networks specialize to each embodiment. O...
| Authors | Richard Li, Aditya Prakash, Andrew Wen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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