ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data
Achieving versatile and natural whole-body humanoid interaction control remains challenging due to the high cost of whole-body teleoperation data. We present ZeroWBC, a teleoperation-free framework that learns humanoid whole-body interaction from human egocentric videos paired with synchronized whole-body motion and text annotations. ZeroWBC adopts a generation-then-tracking formulation to tackle the static scene whole-body interaction control problem. Given an initial egocentric image and a language instruction, a fine-tuned Vision-Language Model generates future human whole-body motion tokens, which are decoded into continuous motions and retargeted to the humanoid. The resulting refere...
| Authors | Haoran Yang, Jiacheng Bao, Yucheng Xin et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 March 2026 |
| Last updated at source | 24 June 2026 |
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