DiMuRe: Diagnosis-Guided Multilevel Recovery for Robotic Task Execution
Overview
This research presents DiMuRe, a framework enabling robotic agents to recover from disturbances by selecting between program reuse and predefined recovery skills using phase-specific evidence. The system utilizes a multimodal large language model (MLLM) to generate corrective code after the original program exits, with local verification ensuring safety. Evaluated on 801 disturbed cases in the LIBERO-Disturb benchmark, Local DiMuRe achieves a 77.65% complete-task success rate compared to 58.68% for the original program, while Full DiMuRe reaches 81.90% with terminal replanning.
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- arXiv Robotics — research abstractsSignalDiMuRe: Diagnosis-Guided Multilevel Recovery for Robotic Task Execution
This research presents DiMuRe, a framework enabling robotic agents to recover from disturbances by selecting between program reuse and predefined recovery skills using phase-specific evidence. The system utilizes a multimodal large language model (MLLM) to generate corrective code after the original program exits, with local verification ensuring safety. Evaluated on 801 disturbed cases in the LIBERO-Disturb benchmark, Local DiMuRe achieves a 77.65% complete-task success rate compared to 58.68% for the original program, while Full DiMuRe reaches 81.90% with terminal replanning.
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