CRII: CPS: A Bi-Trust Framework for Collaboration-Quality Improvement in Human-Robot Collaborative Contexts

协作机器人已被广泛用于在越来越多的领域协助人类。就像人与人的协作一样，对人-机器人团队的信任具有双向属性。然而，在一个统一的人-机器人协作框架中同时研究人-机器人信任问题和机器人-信任-人类问题的研究还很少。该项目通过开发一个新的系统的双向信任框架来应对这一挑战，将人类对机器人的信任和机器人对人类的信任整合到人与机器人的合作过程中。在已建立的双向信任框架的基础上，建立了基于信任级别的计算协作模型，以优化和规划机器人的动作。提出的方法将减少不确定的失败，提高人-机器人共享任务的协作质量。本项目的目标是为人-机器人团队开发一个系统的双信任框架，并创建新的信任动力学和基于信任级别的协作计算模型，以增强人-机器人的伙伴关系。提出的研究活动包括：(1)调查影响人类对机器人信任的因素，并利用多通道物理和生理数据分析人类对机器人的信任；(2)为人-机器人团队开发一个新的双信任框架，该框架包括计算人-机器人信任模型和机器人-信任-人类模型，并通过信任-局部-最大值方法建立计算人-机器人协作模型；(3)将双信任框架应用于真实的人-机器人协作环境并评估其有效性。该项目的发现将提高人-机器人团队在协作环境中的协作质量，如新一代智能制造，并可能通过促进机器人和人工智能劳动力的增加而受益于国家经济增长。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Collaborative robots have been widely employed to assist humans in an increasing number of areas. Just as human-human collaboration, the trust in human-robot teams has a property of bidirectional. However, few studies have been conducted on both human-trusting-robot issue and robot-trusting-human issue in a unified framework for human-robot collaboration. The project addresses this challenge by developing a new systematic Bi-Trust framework to integrate humans’ trust in robots and robots’ trust in humans into the human-robot collaboration process. With the established Bi-Trust framework, a trust-level-based computational collaboration model is created to optimize and plan robot actions. The proposed approaches will reduce uncertain failures and improve the collaboration-quality of human-robot shared tasks.The goal of this project is to develop a systematic Bi-Trust framework for human-robot teams and create new computational models of trust dynamics and trust-level-based collaboration in order to enhance the human-robot partnership. The proposed research activities include: (1) investigating the factors that affect humans’ trust in robots and analyzing humans’ trust using multimodal physical and physiological data; (2) developing a new Bi-Trust framework entailing computational human-trusting-robot model and robot-trusting-human model for human-robot teams and establishing a computational human-robot collaboration model via the trust-local-maximum method; and (3) applying the Bi-Trust framework in real-world human-robot collaborative contexts and assessing its effectiveness. The findings of this project will improve the collaboration-quality for human-robot teams in collaborative contexts such as the new generation of smart manufacturing and potentially benefit the national economic growth by fostering increasing robotics and AI workforce.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.