Date of Award

1-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

College/School

College of Science and Mathematics

Department/Program

Computer Science

Thesis Sponsor/Dissertation Chair/Project Chair

Weitian Wang

Committee Member

Dajin Wang

Committee Member

John Jenq

Abstract

Robots are increasingly being employed for diverse applications where they must work and coexist with humans. The trust in human-robot collaboration (HRC) is a critical aspect of any shared task performance for both the human and the robot. The study of human-trusting-robot has been investigated by numerous researchers. However, robot-trusting-human, which is also a significant issue in HRC, is seldom explored in the field of robotics. In this paper we propose a novel trust-assist framework for human-robot co-carry tasks. This framework allows the robot to determine a trust level on the human co-carry partner. The calculations of this trust level are based on human motions, past interactions between the human-robot pair, and the human's current performance in the co-carry task. The trust level between the human and robot is evaluated dynamically throughout the collaborative task which allows the trust level to change if the human performs false positive motions. Additionally, the proposed framework can enable the robot to generate and perform assisting movements to follow human carrying motions and paces when the human is considered trustworthy in the co-carry task. The results of our experimentation with this framework show that the robot effectively assisted the human in real-world collaborative tasks through the proposed computational trust model.

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