Human trust in robots is one of the key factors in Human-Robot Interaction and a prerequisite for effective Human-Robot Collaboration. Trust characterizes the reliance and tendency of human in using robots. Over-reliance or under-reliance might occur due to improper trust, which results in poor team collaboration, and hence higher task load and lower overall task performance. Inspired by well-known human factors research, I developed a time-series trust model for human-robot collaboration tasks, which is a function of prior trust, robot performance, and human performance. The robot performance is evaluated by its flexibility to keep pace with the human coworker and is molded as the difference between human and robot speed. The human performance in doing physical tasks is directly related to his/her muscle fatigue level. We use the muscle fatigue and recovery dynamics to capture the fatigue level of the human body when performing repetitive kinesthetic tasks, which are typical types of human motions in manufacturing. We experimentally validate our model and control schemes by conducting a series of human-in-the-loop experiments using Rethink Robotics Baxter robot [ref].

I proposed a novel framework for integrating HRI factors into the robot motion controller for human-robot collaborative assembly tasks in a manufacturing hybrid cell [ref]. I developed an optimal control policy that meets human physical demands by coordinating the robot motion progress with respect to human motion progress. I further augment social HRI into the framework by considering a computational model of the human worker's trust in his/her robot partner as well as robot facial expressions. The human worker's trust in robot is computed and used as a metric for path selection as well as a constraint in the optimal control problem. Robot facial expression is displayed for providing additional visual feedbacks to the human worker. I evaluated the proposed framework by designing a robotic experimental testbed and conducting a comprehensive study with a human-in-the-loop.