Abstract
The anticipated social capabilities of robots may allow them to serve in authority roles as part of human-machine teams. To date, it is unclear if, and to what extent, human team members will comply with requests from their robotic teammates, and how such compliance compares to requests from human teammates. This research examined how the human-likeness and physical embodiment of a robot affect compliance to a robot's request to perseverate utilizing a novel task paradigm. Across a set of two studies, participants performed a visual search task while receiving ambiguous performance feedback. Compliance was evaluated when the participant requested to stop the task and the coach urged the participant to keep practicing multiple times. In the first study, the coach was either physically co-located with the participant or located remotely via a live-video. Coach type varied in human-likeness and included either a real human (confederate), a Nao robot, or a modified Roomba robot. The second study expanded on the first by including a Baxter robot as a coach and replicated the findings in a different sample population with a strict chain of command culture. Results from both studies showed that participants comply with the requests of a robot for up to 11 min. Compliance is less than to a human and embodiment and human-likeness on had weak effects on compliance.
Highlights
In the future, it is anticipated that robots will possess highly developed computational and social capabilities that will allow them to act in sophisticated roles in mixed human-machine teams (e.g., Endsley, 2015)
Participants assigned to a robot coach could have chosen to work more or less diligently at the practice task relative to those assigned to a human coach from the outset of the experiment
A series of 2 × 3 between-subjects ANOVAs were computed comparing the duration in seconds that participants performed the Synthetic Aperture Radar (SAR) image task from the beginning of the experiment until their first request to advance to the testing phase, the number of SAR images they examined during that period, their mean inspection time per image, and their detection accuracy
Summary
It is anticipated that robots will possess highly developed computational and social capabilities that will allow them to act in sophisticated roles in mixed human-machine teams (e.g., Endsley, 2015). These collaborative robots will likely interact with and operate alongside human teammates in a number of settings, including education (e.g., Yorita and Kubota, 2010), manufacturing (e.g., Gombolay et al, 2015), healthcare (e.g., Han et al, 2017), and defense. Fraune et al (2017) found that when people were teamed with a robot in a competitive game they demonstrate an in-group bias, attributing greater positive characteristics to their robot teammate, and even applying greater punishment to humans on the rival team to spare their robot teammate
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