提出可执行自然语言指令的人形机器人全身控制框架，实现安全高效的任务操作。
HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
- 通过多教师蒸馏构建模块化控制器，支持运动跟踪、行走与跌倒恢复
- 在Unitree G1上实现接近顶尖的轨迹追踪性能和大范围操作空间
- 无需任务特调数据，即可通过视觉语言模型驱动真实硬件执行复杂任务
为了让类人机器人在真实世界中部署，命令空间（即任务规划与全身控制之间的接口）的选择至关重要。现有全身控制器通常需要密集的运动学或空间参考，而规划器难以从任务语义中生成这些信息。本文提出一种紧凑、显式且直观的接口，具备通用性、模块性和表达力，适用于多种移动-操作技能。为此，我们引入HANDOFF——一个遵循该接口的单一人形全身控制器，通过上下文条件门控机制下的多教师知识蒸馏，由三个互补专家（带安全过滤数据的全身运动跟踪、行走、跌倒恢复）训练而成。在Unitree G1上，HANDOFF实现了接近当前最优的速率追踪性能，并拥有目前最大的稳健操作工作空间。我们进一步通过多个由自然语言驱动的任务演示验证了硬件可行性，系统由视觉语言模型驱动的代理规划器实现，无需任何任务特定数据或控制器微调。
原文摘要 · Abstract (English)
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 loco-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.
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