[Submitted on 6 Oct 2026]
Title:Toward Evidence-Driven Human-Agent-Robot Teaming for Earth-Independent Anomaly Triage
View a PDF of the paper titled Toward Evidence-Driven Human-Agent-Robot Teaming for Earth-Independent Anomaly Triage, by Ignacio G Lopez-Francos and 2 other authors
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Abstract: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.
Comments: Accepted to the Space Robotics Workshop at 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2610.08933 [cs.RO]
(or arXiv:2610.08933v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.08933
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ignacio G Lopez-Francos [view email] [v1] Tue, 6 Oct 2026 18:03:09 UTC (1,019 KB)
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