外观
论文arXiv 2610.1689516 天前
观察、共享还是预测？解耦具身多智能体协作中的伙伴信息
原文链接：Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration（arXiv 2610.16895 · 发布日期：2026-10-12）
Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration
研究无对话具身协作中伙伴状态、知识交换和动作预测的作用，并在Habitat的PARTNR基准上验证。
核心要点
- 探讨LLM具身控制器在不共享意图、计划或任务消息时是否支持协作推理。
- 提出Decoupled Partial Observability，并为零样本ReAct规划器加入OAS、KE和OANA。
- 在四个规划器、1,000个验证任务上，OAS提升任务质量，KE最多减少25%仿真步数。
- OANA在15/16个模型-上下文组合中降低成功率，协调收益主要来自共享上下文。
验证范围与局限：验证限于Habitat-based PARTNR基准的仿真环境、四个规划器和1,000个验证任务。
英文摘要 (Abstract)
LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating a collaborator's actions in real-time teamwork without dialogue. We ask if observing the partner's state, exchanging object knowledge, and predicting the partner's next action, shape task outcomes and coordination when agents can share percepts but never intentions, plans, or task-level messages, and whether the two agents occupy symmetric roles. Using the Habitat-based PARTNR benchmark, we introduce a Decoupled Partial Observability regime that enforces strictly individual perception, and we augment decentralized zero-shot ReAct planners with Other Agent's State (OAS) observation, object Knowledge Exchange (KE) between co-located agents, and Other Agent's Next Action (OANA) prediction, evaluated on the full 2 x 2 x 2 grid with four planners on 1,000 validation tasks. OAS observation improves task quality, reducing extraneous effort for every planner, whereas KE primarily improves execution efficiency, cutting simulation steps by up to 25% with little change in success. The two are largely redundant for strong planners but complementary for the weakest, for which KE without the OAS is the worst configuration. OANA prediction lowers the success rate in 15 of 16 model x context pairs while raising explicit partner mentions to 55-100%, and its accuracy gains lie mainly in the coarse action type, partly because agents recognize the action the partner is already performing. The robot retrieves objects sooner and completes its tasks earlier than the simulated human. Shared context reduces total task time by shortening the remaining duration that the human works alone. Also, coordination benefits arise from responding to shared context, especially the OAS, rather than explicit anticipation, yielding shallow, planner-dependent partner modeling.
作者：Adrián Szlatincsán, Filippo Ziliotto, Bruno Lepri, Luciano Serafini, Massimiliano Luca, Tommaso Campari
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