ReAP: 視覚的失敗認識から行動条件付きリスク予測へ、実行時失敗監視のためのフレームワーク
ReAP: From Visual Failure Recognition to Action-Conditioned Risk Prediction for Runtime Failure Monitoring
ロボット政策の実行時失敗を予測するため、視覚的失敗検出と行動条件付き将来リスク予測を組み合わせた監視フレームワークReAPを提案し、シミュレーションと実世界で検出精度と警告の適時性のバランスを実証した。
著者: Jingyuan Liu, Yunpeng Qing, Changxu Wei, Zhi Hou
分類: cs.RO
原文アブストラクト
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.