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

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.