HIGenNTO: Scalable Humanoid Interaction Generation via Noise-Space Trajectory Optimization
Humanoid robots can acquire complex skills by imitating kinematic humanoid motion references, yet reliable references for contact-rich interactions remain difficult to obtain: motion capture deteriorates under occlusion and close physical contact, while retargeting introduces additional contact and geometric inconsistencies. We present HIGenNTO, a framework that synthesizes humanoid-scene interaction motion references by optimizing the initial noise of a pretrained text-conditioned motion model under sparse spatiotemporal and scene constraints. The same formulation satisfies desired contacts, avoids collisions, and maintains stable support while retaining the prior's realism and temporal ...
| Authors | Lalit Jayanti, Kashu Yamazaki, Yuto Shibata et al. |
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| Primary category | cs.RO |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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