The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong reasoning abilities on textual inputs. To leverage the power of LLMs for robot failure explanation, we introduce REFLECT, a framework which queries LLM for failure reasoning based on a hierarchical summary of robot past experiences generated from multisensory observations. The failure explanation can further guide a language-based planner to correct the failure and complete the task. To systematically evaluate the framework, we create the RoboFail dataset with a variety of tasks and failure scenarios. We demonstrate that the LLM-based framework is able to generate informative failure explanations that assist successful correction planning.

翻译：自动检测和分析失败执行的能力对于构建可解释且鲁棒的机器人系统至关重要。近年来，大语言模型在文本输入上展现出强大的推理能力。为利用大语言模型进行机器人失败解释，我们提出REFLECT框架，该框架基于多模态感知数据生成的机器人历史经验分层摘要，查询大语言模型进行失败推理。失败解释可进一步引导基于语言规划器纠正失败并完成任务。为系统评估该框架，我们创建了包含多种任务和失败场景的RoboFail数据集。实验表明，该基于大语言模型的框架能够生成信息丰富的失败解释，从而辅助成功的纠正规划。