做机器人策略部署或安全检测的团队，终于有了一个不依赖模型内部状态、零运行时开销的失败预警方案——ActProbe 只需动作序列就能提前发现异常，建议做真实机器人实验的开发者直接试。
生成式机器人策略在部署时可能突然失败，现有检测方法需要白盒访问或增加计算开销。ActProbe 提出仅从动作空间提取两个信号——连续动作块间的时间一致性误差（TCE）和当前动作块幅度（ACM），通过单次前向传播即可预测失败。该方法在多个基准测试中将失败检测的F1-时效性帕累托前沿平均提升12.7%，在未见任务上早期检测ROC-AUC领先9.0%。ActProbe 还能迁移到真实机器人拾取任务，将强化学习微调所需环境交互次数减少2.9倍。
ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies
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.