Researchers are beginning to transition from studying human–automation interaction to human–autonomy teaming. This distinction has been highlighted in recent literature, and theoretical reasons why the psychological experience of humans interacting with autonomy may vary and affect subsequent collaboration outcomes are beginning to emerge (de Visser et al., 2018;Wynne and Lyons, 2018). In this review, we do a deep dive into human–autonomy teams (HATs) by explaining the differences between automation and autonomy and by reviewing the domain of human–human teaming to make (...) inferences for HATs. We examine the domain of human–human teaming to extrapolate a few core factors that could have relevance for HATs. Notably, these factors involve critical social elements within teams that are central (as argued in this review) for HATs. We conclude by highlighting some research gaps that researchers should strive toward answering, which will ultimately facilitate a more nuanced and complete understanding of HATs in a variety of real-world contexts. (shrink)

Prior research has demonstrated that trust in robots and performance of robots are two important factors that influence human–autonomy teaming. However, other factors may influence users’ perceptions and use of autonomous systems, such as perceived intent of robots and decision authority of the robots. The current study experimentally examined participants’ trust in an autonomous security robot (ASR), perceived trustworthiness of the ASR, and desire to use an ASR that varied in levels of decision authority and benevolence. Participants (N = 340) (...) were recruited from Amazon Mechanical Turk. Results revealed the participants had increased trust in the ASR when the robot was described as having benevolent intent compared to self-protective intent. There were several interactions between decision authority and intent when predicting the trust process, showing that intent may matter the most when the robot has discretion on executing that intent. Participants stated a desire to use the ASR in a military context compared to a public context. Implications for this research demonstrate that as robots become more prevalent in jobs paired with humans, factors such as transparency provided for the robot's intent and its decision authority will influence users’ trust and trustworthiness. (shrink)

Prior research has demonstrated that trust in robots and performance of robots are two important factors that influence human–autonomy teaming. However, other factors may influence users’ perceptions and use of autonomous systems, such as perceived intent of robots and decision authority of the robots. The current study experimentally examined participants’ trust in an autonomous security robot (ASR), perceived trustworthiness of the ASR, and desire to use an ASR that varied in levels of decision authority and benevolence. Participants (N = 340) (...) were recruited from Amazon Mechanical Turk. Results revealed the participants had increased trust in the ASR when the robot was described as having benevolent intent compared to self-protective intent. There were several interactions between decision authority and intent when predicting the trust process, showing that intent may matter the most when the robot has discretion on executing that intent. Participants stated a desire to use the ASR in a military context compared to a public context. Implications for this research demonstrate that as robots become more prevalent in jobs paired with humans, factors such as transparency provided for the robot's intent and its decision authority will influence users’ trust and trustworthiness. (shrink)

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, 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. (shrink)