ロボットもマルチタスクをこなせる：記憶アーキテクチャとLLMを統合したクロスタスク行動生成
Robots Can Multitask Too: Integrating a Memory Architecture and LLMs for Enhanced Cross-Task Robot Action Generation
人間の認知に着想を得た二層の記憶モデルと2つのLLMを組み合わせ、ロボットが複数タスク間を切り替えながら適応的に行動を生成する手法を提案し、5つのタスクで性能向上を示した。
著者: Hassan Ali, Philipp Allgeuer, Carlo Mazzola, Giulia Belgiovine, Burak Can Kaplan, Lukáš Gajdošech, Stefan Wermter
分類: cs.RO, cs.AI
原文アブストラクト
Large Language Models (LLMs) have been recently used in robot applications for grounding LLM common-sense reasoning with the robot's perception and physical abilities. In humanoid robots, memory also plays a critical role in fostering real-world embodiment and facilitating long-term interactive capabilities, especially in multi-task setups where the robot must remember previous task states, environment states, and executed actions. In this paper, we address incorporating memory processes with LLMs for generating cross-task robot actions, while the robot effectively switches between tasks. Our proposed dual-layered architecture features two LLMs, utilizing their complementary skills of reasoning and following instructions, combined with a memory model inspired by human cognition. Our results show a significant improvement in performance over a baseline of five robotic tasks, demonstrating the potential of integrating memory with LLMs for combining the robot's action and perception for adaptive task execution.
