Mechanical Engineering Department, National Taiwan University, Taipei 10617, Taiwan.
Sensors (Basel). 2024 May 22;24(11):3311. doi: 10.3390/s24113311.
This study develops a comprehensive robotic system, termed the robot cognitive system, for complex environments, integrating three models: the engagement model, the intention model, and the human-robot interaction (HRI) model. The system aims to enhance the naturalness and comfort of HRI by enabling robots to detect human behaviors, intentions, and emotions accurately. A novel dual-arm-hand mobile robot, Mobi, was designed to demonstrate the system's efficacy. The engagement model utilizes eye gaze, head pose, and action recognition to determine the suitable moment for interaction initiation, addressing potential eye contact anxiety. The intention model employs sentiment analysis and emotion classification to infer the interactor's intentions. The HRI model, integrated with Google Dialogflow, facilitates appropriate robot responses based on user feedback. The system's performance was validated in a retail environment scenario, demonstrating its potential to improve the user experience in HRIs.
本研究开发了一种综合机器人系统，称为机器人认知系统，用于复杂环境，集成了三个模型：参与模型、意图模型和人机交互 (HRI) 模型。该系统旨在通过使机器人能够准确检测人类行为、意图和情绪，提高 HRI 的自然性和舒适性。设计了一种新型的双臂手持移动机器人 Mobi 来演示该系统的功效。参与模型利用眼动、头部姿势和动作识别来确定交互启动的合适时刻，解决潜在的眼神接触焦虑问题。意图模型采用情感分析和情绪分类来推断交互者的意图。HRI 模型与 Google Dialogflow 集成，根据用户反馈促进适当的机器人响应。该系统在零售环境场景中的性能得到了验证，展示了其在改善 HRI 用户体验方面的潜力。
