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Toward Evidence-Driven Human-Agent-Robot Teaming for Earth-Independent Anomaly Triage

Toward Evidence-Driven Human-Agent-Robot Teaming for Earth-Independent Anomaly Triage

Ignacio G Lopez-Francos, Alexis Gallagher, Samira Shalal · N/A · 2026

reinforcement-learning control
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Summary

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.

Abstract Summary

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.

Key Points

  • 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.

Evidence-Driven Agentic Architecture

The architecture continuously receives structured events from caution-and-warning systems, conventional fault detection, isolation, and recovery (FDIR), anomaly detectors, and crew reports. An off-nominal event initiates triage; independently designed automated safing may act in parallel when delay is unacceptable.

The architecture then supports situation assessment and early response, when additional evidence can distinguish plausible causes and guide the next safe action.

Evidence-Driven Agentic Architecture — Toward Evidence-Driven Human-Agent-Robot Teaming for Earth-Independent Anomaly Triage
Fig. 1: Human–Agent–Robot architecture and proof-of-concept components. The Agentic Orchestrator coordinates the crew interface and mobile inspection robot with authoritative triage state and bounded services, while system-governed checks and crew authorization constrain operational actions.

Illustrative Use Cases

This scenario uses an actual ISS event to examine how a similar anomaly could be handled aboard a crewed vehicle in transit to Mars, where communication delay prevents real-time support from Mission Control. On January 14, 2015, the ISS annunciated TOXIC ATMOSPHERE Node 2 LTL IFHX NH3 Leak Detected , indicating possible ammonia ingress through the Node 2 Low Temperature Loop Interface Heat Exchanger (IFHX).

Software inhibited ventilation, and the crew isolated in the Russian Segment.

Proof-of-Concept Implementation and Demonstration

We implemented the Section II architecture on an NVIDIA DGX Spark for the Section III-A ammonia scenario. An OpenClaw-based orchestrator routes high-level workflow messages among Reachy Mini as the crew-facing interface, an authoritative triage state manager and bounded backend services, and Innate MARS through a ROS2 robot-tasking service [ 12 , 13 ] .

Local models provide dialogue and tool use ( Gemma-4-26B-A4B-it ), speech recognition and synthesis ( Parakeet-TDT-0.6B-v2 , Kokoro-82M ), and visual interpretation ( Qwen3-VL-8B-Instruct ).

Sources and Demonstrations

Figures are reproduced from Lopez-Francos et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.

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