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 operator during deployment. AutoIntervene evaluates proposed chunks against a visual-action support memory built from successful task executions, combining visual similarity with consistency between pro...
| Authors | Jinhe Tang, Weiming Zhi |
|---|---|
| Primary category | cs.RO |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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