反思：對機器人經驗進行失敗解釋和修正的總結

摘要

自動檢測和分析失敗執行的能力對於具有可解釋性和強韌性的機器人系統至關重要。最近，大型語言模型（LLMs）已在文本輸入上展示出強大的常識推理能力。為了利用LLM的能量來解釋機器人失敗，我們提出了一個名為REFLECT的框架，將多感官數據轉換為機器人過去經驗的分層摘要，並使用漸進式失敗解釋算法向LLM發出查詢。根據解釋，失敗修正計劃者生成一個可執行計劃，使機器人能夠糾正失敗並完成任務。為了系統地評估這個框架，我們創建了RoboFail數據集，並展示了我們基於LLM的框架能夠生成有助於成功糾正計劃的信息豐富的失敗解釋。項目網站：https://roboreflect.github.io/

English

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 common sense reasoning skills on textual inputs. To leverage the power of LLM for robot failure explanation, we propose a framework REFLECT, which converts multi-sensory data into a hierarchical summary of robot past experiences and queries LLM with a progressive failure explanation algorithm. Conditioned on the explanation, a failure correction planner generates an executable plan for the robot to correct the failure and complete the task. To systematically evaluate the framework, we create the RoboFail dataset and show that our LLM-based framework is able to generate informative failure explanations that assist successful correction planning. Project website: https://roboreflect.github.io/

反思：對機器人經驗進行失敗解釋和修正的總結

REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction

摘要

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