ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies

• URL: http://arxiv.org/abs/2606.08508v1
• Date: Sun, 07 Jun 2026 08:18:11 GMT
• Title: ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies
• Abstract summary: We introduce ActProbe, a pure action-space detector for generative robot policies.<n>It uses two compact signals available from a single forward pass: Temporal Consistency Error (TCE) between consecutive action chunks and Action Chunk Magnitude (ACM) of the current chunk.<n>It maps these signals to per-step failure probabilities with a task-conditioned LSTM-MLP architecture.
• Score: 21.31011797815255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions. Existing online failure detectors either require white-box access to policy internals or add runtime overhead through resampling and observation-side signals. Our empirical analysis shows that emitted action chunks themselves already carry strong predictive signal for impending failures in generative robot policies. Motivated by this observation, we introduce ActProbe, a lightweight, pure action-space detector that uses two compact signals available from a single forward pass: Temporal Consistency Error (TCE) between consecutive action chunks and Action Chunk Magnitude (ACM) of the current chunk. ActProbe maps these signals to per-step failure probabilities with a task-conditioned LSTM-MLP architecture. Across a diverse suite of generative robot policies and benchmarks, ActProbe raises alerts before failures become visually recognizable, improving the accuracy (F1)-timeliness Pareto frontier of failure detection by an average hypervolume gain of +12.7% over both internal- and external-feature baselines, with a +9.0% early-detection ROC-AUC lead on unseen tasks. ActProbe further transfers to deployment, predicting failures on unseen real-robot pick tasks and accelerating RL fine-tuning (PPO) with 2.9x fewer environment interactions.

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