柔軟な足場かけリフレクションによるロボットタスク計画フレームワーク
FISRF: Flexible Instructional Scaffolding Reflection Framework for Robotics Task Planning with Large Language Model
LLMによるロボットタスク計画の誤り訂正で、エージェントとメンター構成により発達の最近接領域を推定し、振り返り方略と助言を適応的に選ぶFISRFを提案。誤り訂正回数37%減、冗長推論25.1%減、成功率8%向上を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Qingmiao Liang, Jiatao Zhang, Runxin Zou, Yufan Song, Wei Song, Yuhua Zheng
分類: cs.RO
原文アブストラクト
Large Language Models (LLMs) are widely adopted for reflective error correction in task planning; however, existing approaches primarily rely on autonomous exploration, rendering them prone to accumulating erroneous experiences during early interactions with new environments. To address this limitation, we propose the Flexible Instructional Scaffolding Reflection Framework (FISRF), an agent--mentor architecture that estimates the robot's Zone of Proximal Development (ZPD) in task planning and adaptively selects reflection strategies and mentor guidance, thereby facilitating the accumulation of effective error-correction experience. Experiments in ALFWorld and on real-world robotic platforms demonstrate that FISRF reduces the average number of error-correction rounds by 37% and redundant reasoning by 25.1%, while improving the overall task success rate by 8%. Project webpage : https://fisrfplanning.github.io/
