AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies

Action-chunking visuomotor policies learn from demonstrations and improve temporal consistency by predicting short action sequences rather than single-step commands. Yet perception errors and execution drift can move the robot outside the demonstration distribution, while the policy continues to produce smooth action chunks that are inconsistent with the observed state. We present AutoIntervene, an online framework that selectively transfers control between an action-chunking policy and an opera

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ethics.ai (7 August 2026), “AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies,” evidence record 17810, https://ethics.ai/record/17810 (originally published by arXiv).

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