Date of Award

5-2017

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Mechanical Engineering

Committee Member

Dr. Yue Wang, Committee Chair

Committee Member

Dr. John Wagner

Committee Member

Dr. Yongqiang Wang

Abstract

Human-robot collaboration (HRC) systems integrate the strengths of both humans and robots to improve the joint system performance. In this thesis, we focus on social human-robot interaction (sHRI) factors and in particular regret and trust. Humans experience regret during decision-making under uncertainty when they feel that a better result could be obtained if chosen differently. A framework to quantitatively measure regret is proposed in this thesis. We embed quantitative regret analysis into Bayesian sequential decision-making (BSD) algorithms for HRC shared vision tasks in both domain search and assembly tasks. The BSD method has been used for robot decision-making tasks, which however is proved to be very different from human decision-making patterns. Instead, regret theory qualitatively models human's rational decision-making behaviors under uncertainty. Moreover, it has been shown that joint performance of a team will improve if all members share the same decision-making logic. Trust plays a critical role in determining the level of a human's acceptance and hence utilization of a robot. A dynamic network based trust model combing the time series trust model is first implemented in a multi-robot motion planning task with a human-in-the-loop. However, in this model, the trust estimates for each robot is independent, which fails to model the correlative trust in multi-robot collaboration. To address this issue, the above model is extended to interdependent multi-robot Dynamic Bayesian Networks.

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