EgoSpeedUp: Transferring Human Manipulation Tempo to Robot Policies
Robot manipulation policies trained through imitation learning inherit not only the demonstrated behavior but also the conservative execution tempo of robot demonstrations. Existing acceleration approaches can execute faster than the original demonstrations, but determine the appropriate acceleration primarily from robot-side information or a predefined set of tempo factors, leaving open how to obtain a task-appropriate reference for how fast each manipulation phase should progress. We introduce EgoSpeedUp, a framework that uses human manipulation as temporal supervision for robot imitation learning. Our key insight is that human demonstrations naturally reveal task-appropriate, phase-wis...
| Authors | Hanbit Oh, Yukiyasu Domae, Takuma Yagi |
|---|---|
| Primary category | cs.RO |
| Published | 24 September 2026 |
| Last updated at source | 25 September 2026 |
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