# 通过 Commander-Executor 协调实现可扩展多人体协作操作

> 原文链接：https://robotics.hellobike.com/papers/2026-10-10-2610-14911
> 频道：papers | 更新日期：2026-10-10 | 标签：arxiv, cs.RO
> 核心摘要：针对角色相关、长时序顺序协作操作，提出分层 Commander-Executor 框架，并在多种物体操作任务上验证可扩展协作。

# 通过 Commander-Executor 协调实现可扩展多人体协作操作

::: v-pre
> 原文链接：[Scalable Multi-Human Cooperative Manipulation via Commander-Executor Coordination](https://arxiv.org/abs/2610.14911)（arXiv 2610.14911 · 发布日期：2026-10-10）

**Scalable Multi-Human Cooperative Manipulation via Commander-Executor Coordination**

针对角色相关、长时序顺序协作操作，提出分层 Commander-Executor 框架，并在多种物体操作任务上验证可扩展协作。

## 核心要点

- 现有方法可处理简单协作搬运，但难以应对角色相关、长时序和顺序操作。
- Commander 结合指令与场景信息进行语义和几何规划，并分解角色子任务。
- Executor 基于高层策略和局部观测，通过独自到协作课程学习低层物理策略。
- 实验显示方法可执行组合式长时序任务，并较现有方法获得更高成功率；代码将公开。

**验证范围与局限**：在多种物体操作任务上评估，并展示从双智能体到多智能体协作的扩展。

## 英文摘要 (Abstract)

Multi-human cooperative object manipulation is a highly challenging topic in humanoid control. While existing methods can handle simple collaborative carrying tasks, they struggle with role-dependent, long-horizon, and sequential cooperative manipulation. To address this issue, we propose a hierarchical commander-executor framework that enables multiple agents to accomplish complex collaborative tasks in distinct ways. Specifically, a commander combines instructions and scene information for joint semantic and geometric planning. It then decomposes the task into role-specific subtasks and generates shared object-centric coordination plans, specifying execution timing, target positions, and manipulation actions for each agent. Conditioned on the commander's high-level strategy and each agent's local observations, the executor learns a low-level physics-based policy through a solo-to-cooperative curriculum to realize coordinated human-object interactions. The planned object trajectory provides a shared coordination target, while root positions and grasping instructions specify role-dependent execution cues. We evaluate our method on a variety of object manipulation tasks, demonstrating its ability to execute long-horizon tasks that combine different types of collaboration and to scale from two-agent to multi-agent cooperation. Extensive experiments further show that our approach achieves higher success rates compared to state-of-the-art methods. Code will be made publicly available at https://github.com/TJU-3DV/Co-Manipulation.

作者：Buzhen Huang, Xingchen Wu, Yuanwang Yang, Chenyi Xie, Kun Li
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## 原始参考来源 (Sources)

- [arXiv 2610.14911：Scalable Multi-Human Cooperative Manipulation via Commander-Executor Coordination](https://arxiv.org/abs/2610.14911)