DiMuRe：诊断引导的多层级恢复框架提升机器人任务执行成功率
DiMuRe: Diagnosis-Guided Multilevel Recovery for Robotic Task Execution
机器人任务中途被打断怎么办？DiMuRe 用诊断挑恢复方案，成功率从 58.68% 拉到 81.90%，还带了个新基准 LIBERO-Disturb。
DiMuRe 是面向机器人智能体的恢复框架，利用阶段特定证据和方法前置条件，在程序复用与预定义恢复技能（包括有界 VLA 抓取）之间做选择。当原始程序退出后，多模态大语言模型（MLLM）可基于图像和执行反馈生成纠正代码，并由本地验证检查每次恢复结果。配套发布的 LIBERO-Disturb 基准用于测试执行中扰动下的鲁棒性与恢复能力。在 801 个受扰动的 Encore 开发用例上，Local DiMuRe 完整任务成功率达 77.65%，同物理步数下原始程序为 58.68%；启用终端重规划的 Full DiMuRe 达到 81.90%。
DiMuRe: Diagnosis-Guided Multilevel Recovery for Robotic Task Execution
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.