---
title: "DiMuRe: Diagnosis-Guided Multilevel Recovery for Robotic Task Execution"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.04226v1"
permalink: "https://feed.astralor.com/items/itm_2d7315e1e793f21736a91177"
published: "2026-11-03T03:05:25.000Z"
score: 57
channel: "tech"
tags: ["LLM","AI Agent","多模态","DiMuRe","Diagnosis-Guided Multilevel Recovery","Robotic Task Execution","Disturbances","VLA"]
---

# DiMuRe: Diagnosis-Guided Multilevel Recovery for Robotic Task Execution

## 速览

Disturbances can invalidate the conditions required by a robot's next action even when much of its task program remains useful. A recovery system must therefore choose a response suited to the current state. We present …

## 关键点

- Disturbances can invalidate the conditions required by a robot's next action even when much of its task program remains useful.
- A recovery system must therefore choose a response suited to the current state.
- We present DiMuRe (diagnosis-guided multilevel recovery), a framework for robot-use agents that uses phase-specific evidence and method preconditions to select program reuse or predefined recovery skills, including boun…

## 全文

Disturbances can invalidate the conditions required by a robot's next action even when much of its task program remains useful. A recovery system must therefore choose a response suited to the current state. We present DiMuRe (diagnosis-guided multilevel recovery), a framework for robot-use agents that uses phase-specific evidence and method preconditions to select program reuse or predefined recovery skills, including bounded vision-language-action (VLA) grasping. After the original program exits, a multimodal large language model (MLLM) can generate corrective code from images and execution feedback. Local verification checks the outcome of each recovery attempt. We also introduce LIBERO-Disturb, a benchmark for robustness and recovery under mid-execution disturbances, supporting program-based agents and VLA policies. On 801 scheduled disturbed Encore development cases, Local DiMuRe (program reuse and recovery skills) achieves 77.65% complete-task success, compared with 58.68% for the original program under the same physical-step allowance. Full DiMuRe achieves 81.90% with terminal replanning, an additional step allowance, and early handoff. Local DiMuRe also uses fewer physical steps per scheduled episode than the original program.

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