# 从单智能体到多智能体集群：面向可组合多智能体人-物交互的层级框架

> 原文链接：https://robotics.hellobike.com/papers/2026-10-08-2610-11722
> 频道：papers | 更新日期：2026-10-08 | 标签：arxiv, cs.RO
> 核心摘要：针对单智能体人-物交互策略难以拓展至多智能体协作任务的问题，提出层级框架将单智能体策略转化为可复用技能，在多类HOI任务上验证了有效性。

# 从单智能体到多智能体集群：面向可组合多智能体人-物交互的层级框架

::: v-pre
> 原文链接：[From Solo to Ensemble: A Hierarchical Framework for Composable Multi-Agent Human-Object Interaction](https://arxiv.org/abs/2610.11722)（arXiv 2610.11722 · 发布日期：2026-10-08）

**From Solo to Ensemble: A Hierarchical Framework for Composable Multi-Agent Human-Object Interaction**

针对单智能体人-物交互策略难以拓展至多智能体协作任务的问题，提出层级框架将单智能体策略转化为可复用技能，在多类HOI任务上验证了有效性。

## 核心要点

- 现有多智能体人-物交互方法依赖特定任务微调，高低层逻辑耦合，难以跨不同场景复用。
- 提出层级框架，将单智能体HOI策略蒸馏为可复用的面向对象运动技能，降低任务适配难度。
- 下游任务中冻结底层技能，高层策略基于共享对象等信息生成动作，协调多智能体执行。
- 实验验证该框架支持稳定的代理运动执行，可跨多类场景实现可组合策略学习。

**验证范围与局限**：仅在仿真多类人-物交互任务上完成验证，未开展真机实验。

## 英文摘要 (Abstract)

Physics-based human-object interaction has achieved robust single-agent manipulation skills, yet extending them to multi-agent cooperative tasks remains challenging. Existing approaches typically adapt interaction policies through task-specific fine-tuning, which entangles low-level contact-rich execution with high-level coordination and limits reuse across object geometries, interaction types, and team sizes. We propose a hierarchical framework that converts a single-agent HOI policy into a reusable Object-oriented Motion Skill. Specifically, we reinterpret teacher rollouts as object-oriented action supervision by extracting short-horizon object-proxy motions from executed trajectories, and distill task-specific teachers into a low-level skill operating in an Object-oriented Action Space. For downstream tasks, the distilled skill is frozen as a reusable executor, while a high-level policy coordinates multiple agents by generating region-wise object-oriented actions conditioned on the shared object, task goal, agent states, and local manipulation regions. This formulation shifts multi-agent HOI learning from direct contact-rich full-body control to compact object-level proxy-motion coordination. Experiments on diverse HOI tasks show that the distilled Object-oriented Motion Skill supports robust proxy-motion execution and enables composable policy learning across different interaction types, object geometries, and team sizes.

作者：Zekai Deng, Kangyi Chen, Ye Shi, Jingya Wang
:::

## 原始参考来源 (Sources)

- [arXiv 2610.11722：From Solo to Ensemble: A Hierarchical Framework for Composable Multi-Agent Human-Object Interaction](https://arxiv.org/abs/2610.11722)