发表机构
NASA Ames Research Center; SETI Institute; The University of Texas at Austin; University of Michigan; Repertory Robotics（美国国家航空航天局艾姆斯研究中心; 搜寻地外文明研究所; 德克萨斯大学奥斯汀分校; 密歇根大学; Repertory Robotics）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于证据的权限受限闭环架构，用于深空任务中的人-智能体-机器人协作异常分诊，通过有状态协调器、具身智能体和移动机器人获取证据，并在硬件在环原型中验证。
AI 中文摘要
深空乘员在紧急非标称事件中无法依赖实时地面支持。初始警报可能无法确定原因，而区分性证据可能存在于乘员观察中，或位于对乘员检查而言不安全、成本高昂或不可用的位置。我们提出了一种用于地球独立异常分诊的人-智能体-机器人团队协作的基于证据的架构。智能体AI被视为有界、可检查服务上的有状态协调器，而非完全自主的车辆控制器。分诊状态管理器维护假设、证据来源、不确定性、操作上下文和工具状态；面向乘员的具身智能体引出观察结果并解释评估变化；移动机器人获取有针对性的局部证据。类型化接口将对话和编排与监控、机器人命令、上下文检索和安全关键控制分开。两个场景说明了该架构：一个基于实际国际空间站氨气误报的载人深空任务，其中疑似污染限制了乘员访问；以及一个载人月球基地的电源接口异常，其中机器人检查将局部连接器故障与远程遥测中模糊的其他原因区分开来。我们的主要贡献是一种权限受限的闭环证据架构，在硬件在环集成原型中通过Reachy Mini和Innate MARS移动机器人进行了验证。
英文摘要
Deep-space crews cannot rely on real-time ground support for urgent off-nominal events. Initial alerts may underdetermine cause, while discriminating evidence may reside in crew observations or at locations that are unsafe, costly, or unavailable for crew inspection. We present an evidence-driven architecture for human-agent-robot teaming in Earth-independent anomaly triage. Agentic AI is treated as a stateful coordinator over bounded, inspectable services rather than as a fully autonomous vehicle controller. A triage state manager maintains hypotheses, evidence provenance, uncertainty, operational context, and tool status; a crew-facing embodied agent elicits observations and explains assessment changes; and a mobile robot acquires targeted, localized evidence. Typed interfaces separate dialogue and orchestration from monitoring, robot command, context retrieval, and safety-critical control. Two scenarios illustrate the architecture: a crewed deep-space mission based on an actual ISS ammonia false alarm, where suspected contamination restricts crew access, and a power-interface anomaly at a crewed lunar base, where robotic inspection distinguishes a local connector fault from other causes ambiguous in remote telemetry. Our main contribution is an authority-bounded closed evidence-loop architecture, exercised in a hardware-in-the-loop integration prototype using Reachy Mini and an Innate MARS mobile robot.
CommentsAccepted to the Space Robotics Workshop at 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
