RESCUE: Is This Alarm Worth Acting On? Counterfactual Evaluation of VLA Runtime Monitors
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Abstract (arXiv)
Runtime failure monitors for robot policies are typically evaluated by how accurately they distinguish failed from successful executions. Yet an accurate alarm may arrive too late for recovery, while an unnecessary intervention may disrupt an execution that would otherwise succeed. We introduce RESCUE, a counterfactual framework for evaluating whether a monitor's alarms enable useful interventions. By restoring simulator and policy states at execution checkpoints, RESCUE compares alternative actions, identifies the latest checkpoint satisfying a specified recovery criterion, and evaluates whether acting on alarms reduces expected task-completion cost. We evaluate RESCUE with OpenVLA-OFT and π0.5 over LIBERO-Spatial and LIBERO-Plus using 128,340 counterfactual branches. Recoverability depends on the state--intervention pair rather than on robot state alone: at τ=0.5, 14 of 66 OpenVLA-OFT/LIBERO-Plus failures contain a recoverable checkpoint under continuation, compared with 34 of 66 under continuation or scripted recovery, while recovery reduces recoverability in other conditions. On the primary strong-policy conditions, favourable detection scores do not establish lower intervention cost. Under the evaluated stop-and-reset model, even an idealised detector can afford only a 2.0--3.3% false-alarm rate before unnecessary resets outweigh the savings from stopping failures early. These results show that monitor value depends jointly on the intervention available and the workload in which it is deployed.
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References · 17
- 2025π0.5: a Vision-Language-Action Model with Open-World Generalizationcited by 1,761
- 2023LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learningcited by 1,410
- 2025Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Successcited by 883
- 2025LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Modelscited by 303
- 2017Hamilton-Jacobi Reachability: A Brief Overview and Recent Advancescited by 201
- 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 108
- 2025SAFE: Multitask Failure Detection for Vision-Language-Action Modelscited by 80
- 2024Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progresscited by 69
- 2025Failure Prediction at Runtime for Generative Robot Policiescited by 42
- 2026Accurate Failure Prediction in Agents Does Not Imply Effective Failure Preventioncited by 15
- 2026Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latentscited by 11
- 2026Rewind-IL: Online Failure Detection and State Respawning for Imitation Learningcited by 9
5 more
- 2026Back to the Familiar Future: Failure Recovery for VLA Policies via Pre-Imagined Milestone Selectioncited by 5
- 2026CheckVLA: Execution-Time Verification with Action-Conditioned World Model for Long-Horizon Mobile Manipulationcited by 4
- 2026AEGIS: A Backup Reflex for Physical AIcited by 3
- 2026VLA-FAIL: Efficient Task Failure Detection for Finetuned Vision-Language-Action Modelscited by 3
- 2026PREFAIL: Identifying Precursors to Failures in Robotic Lift-and-Place Tasks to Improve Task Execution Performancecited by 1
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- 2026ReAP: From Visual Failure Recognition to Action-Conditioned Risk Prediction for Runtime Failure Monitoring
- 2026Real-Time Detection and Repair of LLM Agent Failures
- 2026CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding
- 2026When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents
- 2026World Models Dream of Success: Diagnosing and Repairing Failure Insensitivity in Robot World Models
- 2026Kintsugi-VLA: Turning Failed Robot Rollouts into Recovery Data through Interventional Recoverability