aiqbench analysis
The paper proposes a learned runtime monitor for generalist robot policies that pairs a visual failure detector with an action-conditioned predictor, so it can estimate the risk of an action before it executes. The reported evaluation covers simulation and real-world robots and claims a trade-off between detection accuracy and warning lead time. Readers should check how the monitor performs on policies and failure modes outside the tested set, since its value depends on that generalization.
ReAP: From Visual Failure Recognition to Action-Conditioned Risk Prediction for Runtime Failure Monitoring
arXiv:2610.16309v1 Announce Type: new Abstract: Despite rapid progress, generalist robot policies remain brittle during execution. Reliable deployment therefore requires runtime monitoring that provides timely warnings of failure. Existing monitors commonly estimate risk from current observations or policy signals without explicitly predicting the future consequences of proposed actions. To anticipate such risks before action execution, we introduce ReAP, a future-aware runtime monitoring framework that combines visual failure recognition with action-conditioned failure prediction. Specifically, ReAP first trains a visual detector on successful and failed rollouts to learn a failure-relevant representation space and estimate current risk. An action-conditioned predictor then forecasts the resulting future representation in this space to estimate future failure risk. At runtime, the detector and predictor provide complementary signals for current-state failure recognition and future-aware risk assessment. Experiments across diverse robot policies in simulation and the real world demonstrate that ReAP effectively balances failure-detection accuracy and warning timeliness.
Abstract:Despite rapid progress, generalist robot policies remain brittle during execution. Reliable deployment therefore requires runtime monitoring that provides timely warnings of failure. Existing monitors commonly estimate risk from current observations or policy signals without explicitly predicting the future consequences of proposed actions. To anticipate such risks before action execution, we introduce ReAP, a future-aware runtime monitoring framework that combines visual failure recognition with action-conditioned failure prediction. Specifically, ReAP first trains a visual detector on successful and failed rollouts to learn a failure-relevant representation space and estimate current risk. An action-conditioned predictor then forecasts the resulting future representation in this space to estimate future failure risk. At runtime, the detector and predictor provide complementary signals for current-state failure recognition and future-aware risk assessment. Experiments across diverse robot policies in simulation and the real world demonstrate that ReAP effectively balances failure-detection accuracy and warning timeliness.
Robotics (cs.RO)
arXiv:2610.16309 [cs.RO]
(or arXiv:2610.16309v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.16309 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 236 words
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