Reasoning the Trust of Humans in Robots through Physiological Biometrics in Human-Robot Collaborative Contexts
Reasoning the Trust of Humans in Robots through Physiological Biometrics in Human-Robot Collaborative Contexts
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DOI：
10.1109/urtc56832.2022.10002210
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Tiffany Guo;Omar Obidat;Laury Rodriguez;Jesse Parron;Weitian Wang
中科院分区：
文献类型：
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作者：
Tiffany Guo;Omar Obidat;Laury Rodriguez;Jesse Parron;Weitian Wang
随着近年来自动化和人工智能的快速发展，人-机器人协作(HRC)在各个领域发挥着重要作用。人与机器人之间的信任是实现人权委员会效率和成功的重要因素。人类对机器人缺乏信任可能会产生严重的后果，特别是在人类必须适应陌生情况的现实世界应用中。在这项工作中，我们开发了一种新颖而有效的方法，使机器人能够在共享任务期间主动推理和响应动态的人类情感和信任级别。我们在人-机器人对象交接的背景下进行了真实世界的验证实验，表明了机器人能够实时正确地识别和预测人类的信任级别，并相应地帮助人类完成人-机器人协作任务。文中还讨论了如何改进该方法的性能的未来工作。
With the rapid recent growth of automation and artificial intelligence, human-robot collaboration (HRC) is playing a significant role across a variety of fields. Trust between humans and robots is an important element to enable the efficiency and success of HRC. The lack of trust of humans in robots can have critical consequences, especially in real-world applications in which humans must adapt to unfamiliar situations. In this work, we develop a novel and effective approach for robots to actively reason and respond to dynamic human emotions and trust levels during shared tasks. We implement a real-world validation experiment in the context of human-robot object hand-over, which shows the robot’s ability to correctly identify and predict the human’s trust levels in real-time and assist the human accordingly in human-robot collaborative tasks. Future work on how to improve the performance of the proposed approach is also discussed.