Research

Project PI: Weitian Wang

Project Duration: July 2021 - June 2024

Project Description:

​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.

Publications:

• C. Hannum, R. Li, and W. Wang*, "A Trust-Assist Framework for Human-Robot Co-Carry Tasks," Robotics, vol. 12, no. 2, pp. 1-19, 2023.

• C. Conti, A. Varde, and W. Wang*, "Human-Robot Collaboration with Commonsense Reasoning in Smart Manufacturing Contexts," IEEE Transactions on Automation Science and Engineering, pp. 1-13, 2022. (Q1)

• T. Guo, O. Obidat, L. Rodriguez, J. Parron, and W. Wang*, "Reasoning the Trust of Humans in Robots through Physiological Biometrics in Human-Robot Collaborative Contexts," in Proc. The IEEE MIT Undergraduate Research Technology Conference (URTC), 2022, pp. 1-6. (2nd Place Best Paper Award)

• P. Tilloo, J. Parron, O. Obidat, M. Zhu, and W. Wang*, "A POMDP-based Robot-Human Trust Model for Human-Robot Collaboration," in Proc. The 12th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, 2022, pp. 1-6.

• O. Obidat and W. Wang*, "TELL ME YOUR FEELINGS: Characterization and Analysis of Human Comfort in Human-Robot Collaborative Manufacturing Contexts," in 2021 6th International Conference on Control, Robotics and Cybernetics, 2021, pp. 1-5.

• R. Li and W. Wang*, "Augmenting the Communication Naturalness via A 3D Audio-Visual Virtual Agent for Collaborative Robots," in Proc. 2021 IEEE International Conference on Big Data, 2021, pp. 1-3.

• P. Persaud, A. Varde, and W. Wang, “Can Robots Get Some Human Rights? A Cross-Disciplinary Discussion,” Journal of Robotics, pp. 1-11, 2021.