多様なユーザー層に適応する人間-ロボットインタラクションのフレームワーク
A Framework for Adapting Human-Robot Interaction to Diverse User Groups
多様なユーザー層に合わせて対話を調整し、ユーザーが小さな割り込みや大きな変更でインタラクションを制御できるROSベースの適応型HRIフレームワークを開発した。
著者: Theresa Pekarek Rosin, Vanessa Hassouna, Xiaowen Sun, Luca Krohm, Henri-Leon Kordt, Michael Beetz, Stefan Wermter
分類: cs.RO, cs.CL, cs.HC
原文アブストラクト
To facilitate natural and intuitive interactions with diverse user groups in real-world settings, social robots must be capable of addressing the varying requirements and expectations of these groups while adapting their behavior based on user feedback. While previous research often focuses on specific demographics, we present a novel framework for adaptive Human-Robot Interaction (HRI) that tailors interactions to different user groups and enables individual users to modulate interactions through both minor and major interruptions. Our primary contributions include the development of an adaptive, ROS-based HRI framework with an open-source code base. This framework supports natural interactions through advanced speech recognition and voice activity detection, and leverages a large language model (LLM) as a dialogue bridge. We validate the efficiency of our framework through module tests and system trials, demonstrating its high accuracy in age recognition and its robustness to repeated user inputs and plan changes.
