Do as I Do: Dexterous Manipulation Data from Everyday Human Videos
How can we scalably generate data for robotic manipulation, especially on human-like platforms such as dexterous multi-fingered hands? Learning from human videos has recently emerged as a likely answer to this question. However, difficulties in estimating hand-object interaction and crossing the human-to-robot embodiment gap have hindered the adoption of abundant monocular RGB-only human videos as the primary source of robot manipulation data. In this work, we present DO AS I DO, an algorithm to reconstruct and retarget monocular RGB human videos to multi-fingered dexterous robotic hands. DO AS I DO reconstructs hand-object interactions from various egocentric and exocentric in-the-wild v...
| Authors | Bhawna Paliwal, Haritheja Etukuru, William Liang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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