SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework
Building humanoid robots capable of generalizable whole-body loco-manipulation in the real world remains a fundamental challenge. Existing methods either rely on laborious task-specific reward engineering, rigidly replay reference motions that fail to generalize, or depend on costly teleoperation that limits scalability. While human videos capture diverse human behaviors, motion priors inferred from them are inherently imperfect, suffering from occlusion, contact artifacts, and retargeting errors that render them unsuitable for direct policy learning. To address this, we present SUGAR, a scalable data-driven framework that converts diverse human videos into deployable humanoid loco-manipu...
| Authors | Tianshu Wu, Xiangqi Kong, Yue Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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