REFLECT: ロボット経験の要約による失敗説明と修正
REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction
マルチセンサ観測からロボットの過去経験を階層的に要約し、LLMで失敗原因を推論して言語ベースのプランナが修正できるようにするフレームワークREFLECTを提案し、RoboFailデータセットで評価した。
著者: Zeyi Liu, Arpit Bahety, Shuran Song
分類: cs.RO, cs.AI, cs.CL, cs.CV
原文アブストラクト
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.
