おしゃべりするロボット：マルチモーダルな人間-ロボット会話と協働の基盤構築
When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration
LLMを中心に音声認識・音声生成・物体検出・姿勢推定・ジェスチャ検出を統合し、自然な人間-ロボット会話と協働を実現するモジュール型システムを提案した。
著者: Philipp Allgeuer, Hassan Ali, Stefan Wermter
分類: cs.RO
原文アブストラクト
We investigate the use of Large Language Models (LLMs) to equip neural robotic agents with human-like social and cognitive competencies, for the purpose of open-ended human-robot conversation and collaboration. We introduce a modular and extensible methodology for grounding an LLM with the sensory perceptions and capabilities of a physical robot, and integrate multiple deep learning models throughout the architecture in a form of system integration. The integrated models encompass various functions such as speech recognition, speech generation, open-vocabulary object detection, human pose estimation, and gesture detection, with the LLM serving as the central text-based coordinating unit. The qualitative and quantitative results demonstrate the huge potential of LLMs in providing emergent cognition and interactive language-oriented control of robots in a natural and social manner.
