A Framework for Adapting Human-Robot Interaction to Diverse User Groups

TL;DR

This paper introduces an adaptive, open-source framework for human-robot interaction that personalizes engagement for diverse user groups using speech recognition, LLMs, and user feedback, validated through system testing.

Contribution

The paper presents a novel ROS-based adaptive HRI framework supporting natural interaction and user-specific customization, with open-source implementation and validation.

Findings

High accuracy in age recognition

Robustness to repeated inputs and plan changes

Effective adaptation to diverse user groups

Abstract

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.