让机器人根据用户差异自适应调整互动方式，支持自然语音与中断反馈。
A Framework for Adapting Human-Robot Interaction to Diverse User Groups
- 基于ROS构建可扩展的交互框架，融合语音识别与大语言模型对话
- 年龄识别准确率高，对重复输入和计划变更有强鲁棒性
- 适合需要个性化服务的智能客服、教育机器人等场景
为在真实环境中实现与多样化用户群体的自然直观交互，社交机器人需能响应不同群体的需求与期望，并根据用户反馈调整行为。现有研究多聚焦特定人口统计特征，本文提出一种新型自适应人机交互（HRI）框架，可针对不同用户群体定制交互策略，并支持个体通过轻微或重大中断调节互动过程。主要贡献包括：开发了一个基于ROS的开源自适应HRI框架，具备自然交互能力，集成先进语音识别与语音活动检测技术，并利用大语言模型（LLM）作为对话桥梁。通过模块测试与系统试验验证了框架的高效性，结果表明其在年龄识别方面具有高精度，并对重复用户输入及任务计划变更表现出强鲁棒性。
原文摘要 · Abstract (English)
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.
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