# RESETTLE：通过分歧触发检索与高效纠正控制实现机器人恢复

> 原文链接：https://robotics.hellobike.com/papers/2026-10-08-2610-12185
> 频道：papers | 更新日期：2026-10-08 | 标签：arxiv, cs.RO
> 核心摘要：RESETTLE针对机器人操作执行偏差恢复延迟，凭分歧触发检索与纠正控制，在仿真和真实任务中验证提升成功率并降低延迟。

# RESETTLE：通过分歧触发检索与高效纠正控制实现机器人恢复

::: v-pre
> 原文链接：[RESETTLE: Robotic Recovery through Disagreement-Triggered Retrieval and Efficient Corrective Control](https://arxiv.org/abs/2610.12185)（arXiv 2610.12185 · 发布日期：2026-10-08）

**RESETTLE: Robotic Recovery through Disagreement-Triggered Retrieval and Efficient Corrective Control**

RESETTLE针对机器人操作执行偏差恢复延迟，凭分歧触发检索与纠正控制，在仿真和真实任务中验证提升成功率并降低延迟。

## 核心要点

- 重复视觉语言推理或在线优化式恢复延迟较高，可能延误及时干预。
- 框架在冻结策略动作执行接口监测双采样动作持续分歧，并触发恢复。
- 用改造V-JEPA检索同任务示范，结合状态伺服先验与受保护视觉残差执行一次纠正动作。
- 仿真与真实任务中提升成功率，QwenPI比较显示监测恢复延迟更低；代码已开源。

**验证范围与局限**：验证覆盖六个基础策略的仿真、两个策略的四个真实任务及文中比较设置。

## 英文摘要 (Abstract)

Reliable robotic manipulation requires timely intervention to correct emerging deviations and restore progress after execution errors. However, recovery methods based on repeated vision-language reasoning or iterative online optimization can incur substantial latency, delaying intervention. To address these challenges, we introduce RESETTLE(Robotic rEcovery through diSagrEement-Triggered reTrievaL and Efficient Corrective Control), a model-agnostic framework that provides computationally efficient recovery at the action-execution interface of frozen robot policies. RESETTLE triggers recovery when two action proposals independently sampled under identical conditioning persistently disagree. It retrieves a same-task demonstration reference using an adapted V-JEPA encoder and combines a state-servo prior with a guarded visual residual to execute one corrective action without online trajectory optimization or additional vision-language reasoning, then returns control to the base policy. Across six base policies in simulation, RESETTLE achieves up to 8.70%, 6.28%, and 6.83% absolute success-rate gains on LIBERO-Plus, Meta-World, and RoboCasa Tabletop, respectively, with further improvements on four real-world tasks using two policies. In QwenPI-based comparisons, its monitoring-and-recovery computation latency is 74.04%--93.57% lower than VoLoAgent's monitoring-and-planning latency for grasp and place tool calls. It also raises Harness VLA's LIBERO-Pro Swap success from 42% to 50%, demonstrating compatibility with high-level agentic planning. Code available at: https://github.com/JIA-Lab-research/RESETTLE

作者：Yuxin Chen, Senqiao Yang, Zixuan Wang, Jinhui Ye, Changsheng Lu, Pengguang Chen, Shu Liu, Zhuotao Tian, Jiaya Jia
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## 原始参考来源 (Sources)

- [arXiv 2610.12185：RESETTLE: Robotic Recovery through Disagreement-Triggered Retrieval and Efficient Corrective Control](https://arxiv.org/abs/2610.12185)