HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
Humanoid loco-manipulation benefits from one controller to coordinate locomotion, arm movements, and fall recovery. These behaviors are difficult to learn together from scratch because they require different skills and objectives. We present HANDOFF, a training architecture that distills motion-tracking, locomotion, and recovery teachers into one 29-DoF policy, using context-conditioned KL losses over separate action slices and a soft mixture of experts. Commanded velocity continuously blends body supervision, while a binary recovery flag assigns the full action to the recovery teacher. The resulting controller takes a compact 10-D task-space command rather than a dense kinematic stream a...
| Authors | Lizhi Yang, Junheng Li, Nehar Poddar et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 June 2026 |
| Last updated at source | 29 September 2026 |
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