Abstract
Cooperative multi-agent methods for embodied AI are almost universally evaluated under idealized communication: zero latency, no packet loss, and unlimited bandwidth. Real-world deployment on robots with wireless links, autonomous vehicles on congested networks, or drone swarms in contested spectrum offers no such guarantees. We introduce AgentComm-Bench, a benchmark suite and evaluation protocol that systematically stress-tests cooperative embodied AI under six communication impairment dimensions: latency, packet loss, bandwidth collapse, asynchronous updates, stale memory, and conflicting sensor evidence. AgentComm-Bench spans three task families: cooperative perception, multi-agent waypoint navigation, and cooperative zone search, and evaluates five communication strategies, including a lightweight method we propose based on redundant message coding with staleness-aware fusion. Our experiments reveal that communication-dependent tasks degrade catastrophically: stale memory and bandwidth collapse cause over 96% performance drops in navigation, while content corruption (stale or conflicting data) reduces perception F1 by over 85%. Vulnerability depends on the interaction between impairment type and task design; perception fusion is robust to packet loss but amplifies corrupted data. Redundant message coding more than doubles navigation performance under 80% packet loss. We release AgentComm-Bench as a practical evaluation protocol and recommend that cooperative embodied AI work report performance under multiple impairment conditions.
Abstract (translated)
具身AI的多智能体协作方法几乎普遍在理想化通信条件下进行评估：零延迟、无丢包、无限带宽。然而，机器人在无线链路下的实际部署、拥堵网络中的自动驾驶车辆，或 contested spectrum 中的无人机集群都无法提供此类保障。我们推出 AgentComm-Bench 基准套件与评估协议，该系统在六个通信受损维度下对协作式具身AI进行系统性压力测试：延迟、丢包率、带宽崩溃、异步更新、陈旧记忆与冲突传感器证据。AgentComm-Bench 涵盖三类任务家族：协作感知、多智能体航点导航与协作区域搜索，并评估五种通信策略，其中包括我们提出的一种基于冗余消息编码与感知陈旧性的融合轻量级方法。实验表明，依赖通信的任务性能急剧下降：陈旧记忆与带宽崩溃导致导航性能下降超过96%，而内容损坏（陈旧或冲突数据）使感知F1值降低超过85%。脆弱性取决于受损类型与任务设计的交互——感知融合对丢包具有鲁棒性，但会放大损坏数据的影响。在80%丢包率下，冗余消息编码使导航性能提升超两倍。我们发布 AgentComm-Bench 作为实用评估协议，并建议协作式具身AI研究应报告多种通信受损条件下的性能表现。
URL
https://arxiv.org/abs/2603.20285
