A Framework for Adapting Human-Robot Interaction to Diverse User Groups
This work addresses the need for more adaptable human-robot interaction systems for varied user demographics, though it appears incremental by building on existing technologies like ROS and LLMs.
The paper tackles the problem of enabling social robots to adapt interactions to diverse user groups by developing a novel framework that supports natural interactions through speech recognition and LLM-based dialogue, achieving high accuracy in age recognition and robustness to user inputs.
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.