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

2024-10-15

ICSR + AI

AI Summary

To address the need for natural, adaptive human-robot interaction (HRI) with diverse user groups in real-world settings, this paper proposes a two-tier adaptive HRI framework: an upper tier enabling group-level customization (e.g., age-aware adaptation) and a lower tier supporting real-time individual interventions (e.g., voice interruption and fine-grained behavioral adjustment). The framework integrates Whisper, WeNet, a lightweight age classification model, and a locally deployed large language model (LLM) within ROS, with the LLM serving as the robust dialogue orchestrator. Its key contribution is the first implementation of a synergistic adaptive mechanism that jointly optimizes group-level personalization and individual-level responsiveness. Experiments demonstrate 98.2% accuracy in age classification and strong robustness against high-frequency repetitive inputs and on-the-fly task re-planning. The source code is publicly available.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Adapting robot behavior to diverse user needs
• Enabling user-modulated interactions via interruptions
• Validating framework accuracy and robustness

Innovation

• Adaptive ROS-based HRI framework for diverse users
• LLM-powered dialogue bridge for natural interactions
• High-accuracy age recognition and robust feedback handling