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.

翻译：生成式机器人策略在部署时会出现不可预测的故障：它们在关键时刻犹豫不决、偏离任务目标或执行不可恢复的动作。现有的在线故障检测器要么需要白盒访问策略内部结构，要么通过重采样和观测侧信号增加运行时开销。我们的实证分析表明，生成式机器人策略中发出的动作块本身已蕴含即将发生故障的强预测信号。基于这一观察，我们提出ActProbe——一种轻量级的纯动作空间检测器，利用单次前向传播即可获取的两个紧凑信号：连续动作块间的时间一致性误差（Temporal Consistency Error, TCE）和当前动作块的动作块幅度（Action Chunk Magnitude, ACM）。ActProbe通过任务条件化的LSTM-MLP架构将这些信号映射为每步故障概率。在多样化的生成式机器人策略与基准测试套件中，ActProbe能在故障变得视觉可辨前发出警报，相比基于内部和外部特征的基线方法，将故障检测的精度（F1）-时效性帕累托前沿平均超体积增益提升+12.7%，在未见任务上的早期检测ROC-AUC领先+9.0%。ActProbe进一步迁移至实际部署，可在未见过的真实机器人拾取任务中预测故障，并将强化学习微调（PPO）的环境交互次数减少2.9倍。