LLMによる自動シナリオ生成とマルチエージェント制御を備えたロボットガイド
Robot guide with multi-agent control and automatic scenario generation with LLM
大規模言語モデルで行動シナリオを自動生成し、マルチエージェント型の資源管理でロボットの非言語行動を制御する社会ロボット案内システムを開発した。
著者: Elizaveta D. Moskovskaya, Anton D. Moscowsky
分類: cs.RO, cs.LG
原文アブストラクト
The article describes the development of a hybrid social robot control architecture to overcome the limitations of traditional approaches, where behavior scripts manually synchronize the robot's actions and text, and existing methods focus primarily on short dialogue responses. The architecture of the proposed system combines a multi-agent resource management system with automatic generation of behavior scenarios based on large language models. This system automates the preparation of text and commands for the robot's non-verbal behavior for extended narratives and resolves resource conflicts between multiple execution mechanisms. The system was tested on the MENTOR-1 tour guide robot, for which it successfully generated scenarios automatically and demonstrated more natural and rich behavior compared to existing approaches. The proposed approach provides full automation of both scenario preparation and execution through efficient resource management, enhancing the quality of social robot interaction in long-term storytelling tasks.