CoPAL: 大規模言語モデルによるロボット行動の修正計画
CoPAL: Corrective Planning of Robot Actions with Large Language Models
LLMを使ったロボットのタスク・動作計画において、物理的・論理的・意味的な誤りを検出して再計画するフィードバック機構を提案し、ブロック操作やバーマン、ピザ作りで有効性を示した。
著者: Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Stephan Hasler, Daniel Tanneberg, Michael Gienger
分類: cs.RO, cs.AI
原文アブストラクト
In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge. Addressing this imperative, this study contributes to the field of Large Language Models (LLMs) applied to task and motion planning for robots. We propose a system architecture that orchestrates a seamless interplay between multiple cognitive levels, encompassing reasoning, planning, and motion generation. At its core lies a novel replanning strategy that handles physically grounded, logical, and semantic errors in the generated plans. We demonstrate the efficacy of the proposed feedback architecture, particularly its impact on executability, correctness, and time complexity via empirical evaluation in the context of a simulation and two intricate real-world scenarios: blocks world, barman and pizza preparation.
