HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
This work provides a practical interface for task-level planning to control humanoid robots, enabling general manipulation without dense kinematic references.
HANDOFF introduces a compact, explicit command interface for humanoid whole-body control, enabling diverse manipulation skills. It achieves state-of-the-art velocity tracking and one of the largest robust manipulation workspaces on the Unitree G1, demonstrated via natural-language-driven tasks without task-specific data.
For a humanoid robot to be deployed in the real world, the choice of command space (i.e., the interface between task planning and whole-body control) is crucial. Existing whole-body controllers typically demand dense kinematic or spatial references that planners struggle to synthesize from task semantics. We instead propose a compact, explicit interface that is intuitive, general, modular, and expressive enough for diverse manipulation skills. To this end, we introduce HANDOFF, a single humanoid whole-body controller that follows this interface and is distilled via multi-teacher KL distillation under a context-conditioned gating scheme into a mixture-of-experts student from three complementary specialists: whole-body motion tracking with safety-filtered data, locomotion, and fall-recovery. On the Unitree G1, HANDOFF matches state-of-the-art velocity tracking and offers one of the largest robust manipulation workspaces. We further demonstrate hardware feasibility through multiple natural-language-driven task roll-outs, powered by a VLM-driven agentic planner with no task-specific data or controller fine-tuning.