The factors impacting the dynamic development of trust between the human and a cobot can be grouped into three broad categories: Human Factors; Cobot System Factors; and Context Factors. In this project, we plan to address the following research questions: how do users’ emotional states and personality traits impact the development of trust over time? How does cobot’s ability to adapt to human behavior impacts the trust development? In a VR simulation environment, how do the designs of the Avatar impact the development of trust? 

Human-cobot collaborative assembly task design: In our experiment, a task will require human participants to collaborate with a cobot to build an assembly “widget” from a specific set of components.

Measurement of participants’ human factors: To measure participants’ personality traits, we use Gosling et al.’s Ten Item Personality Inventory (TIPI). To measure participants’ emotional intelligence, we use Bar-on’s Emotional Quotient Inventory (EQ-i).

Measurement of participants’ trust development: In addition to the periodic collections of subjective ratings of trust, we’ll use an EEG system to measure the participants’ neural correlates that have been shown to associate with trust development. We analyze the EEG signals and corresponding subjective data to infer the participants’ trust development over time.

Performance metrics for the human-cobot collaboration: The performance metrics will primarily focus on productivity in the assembly task: assembly time, throughput, variability in assembly rates, etc. Work quality metrics will be collected by tallying errors, defects, scrap, and rework.

Safety metrics for the human-cobot collaboration: The main safety risk in this system involves unintended human-cobot collisions. Therefore, the main safety metrics will concern measuring the risk of these collisions