Human-Robot Teaming in Urban Search and Rescue

Bartlett, Cade E.; Cooke, Nancy J.

doi:10.1177/1541931215591051

Cited by 25 publications

(17 citation statements)

References 22 publications

(14 reference statements)

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“…Human trust in automated/robotic technology (henceforth, referred to as trust in automation) is critical to seamless adaptation of technology, and has consequently been of interest to HRI researchers since as early as the 1980s [18]. Issues of trustreliability mis-calibration continue to be active areas of research related to human-robot teaming in its various forms [12,[19][20][21]. Existing research, however, has primarily examined trust as a steady-state measure, typically evaluated through questionnaires administered to human operators at the end of their interaction with automation.…”

Section: Prior Art and Research Aimsmentioning

confidence: 99%

Evaluating Effects of User Experience and System Transparency on Trust in Automation

Yang

Unhelkar

et al. 2017

Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction

155

120

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Existing research assessing human operators' trust in automation and robots has primarily examined trust as a steady-state variable, with little emphasis on the evolution of trust over time. With the goal of addressing this research gap, we present a study exploring the dynamic nature of trust. We defined trust of entirety as a measure that accounts for trust across a human's entire interactive experience with automation, and first identified alternatives to quantify it using real-time measurements of trust. Second, we provided a novel model that attempts to explain how trust of entirety evolves as a user interacts repeatedly with automation. Lastly, we investigated the effects of automation transparency on momentary changes of trust. Our results indicated that trust of entirety is better quantified by the average measure of "area under the trust curve" than the traditional post-experiment trust measure. In addition, we found that trust of entirety evolves and eventually stabilizes as an operator repeatedly interacts with a technology. Finally, we observed that a higher level of automation transparency may mitigate the "cry wolf" effect-wherein human operators begin to reject an automated system due to repeated false alarms.

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Section: Prior Art and Research Aimsmentioning

confidence: 99%

Evaluating Effects of User Experience and System Transparency on Trust in Automation

Yang

Unhelkar

et al. 2017

Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction

155

120

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“…Chen and Barnes (2014) present a comprehensive review of issues pertaining to human-automation interaction and human-agent teaming in the context of robotics. Recent research has focused on developing robots that effectively work with humans (Zhang, Narayanan, Chakraborti, & Kambhampati, 2015), the interactions among humans and robots during teaming (Bartlett & Cooke, 2015), and modeling human cognition in robots (Goodrich & Yi, 2013).…”

Section: Introductionmentioning

confidence: 99%

Teaming With a Synthetic Teammate: Insights into Human-Autonomy Teaming

Demir²,

et al. 2017

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Objective Three different team configurations are compared with the goal of better understanding human-autonomy teaming (HAT). Background Although an extensive literature on human-automation interaction exists, much less is known about HAT in which humans and autonomous agents interact as coordinated units. Further research must be conducted to better understand how all-human teams compare to HAT. Methods In an unmanned aerial system (UAS) context, a comparison was made among three types of three-member teams: (1) synthetic teams in which the pilot role is assigned to a synthetic teammate, (2) control teams in which the pilot was an inexperienced human, and (3) experimenter teams in which an experimenter served as an experienced pilot. Ten of each type of team participated. Measures of team performance, target processing efficiency, team situation awareness, and team verbal behaviors were analyzed. Results Synthetic teams performed as well at the mission level as control (all human) teams but processed targets less efficiently. Experimenter teams performed better across all other measures compared to control and synthetic teams. Conclusion Though there is potential for a synthetic agent to function as a full-fledged teammate, further advances in autonomy are needed to improve team-level dynamics in HAT teams. Application This research contributes to our understanding of how to make autonomy a good team player.

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“…In this case, an autonomous team member is considered to be capable of working alongside human team member(s) by interacting with other team members (Schooley et al, 1993;Krogmann, 1999;Endsley, 2015), making its own decision about its actions during the task, and carrying out taskwork and teamwork (McNeese et al, 2018). In team literature, it is clear that autonomous agents have grown more common in different contexts, e.g., software (Ball et al, 2010) and robotics (Cox, 2013;Goodrich and Yi, 2013;Chen and Barnes, 2014; Bartlett and Cooke, 2015;Zhang et al, 2015;Demir et al, 2018c). However, considering an autonomous agent as a teammate is challenging (Klein et al, 2004) and requires effective teamwork functions (McNeese et al, 2018): understanding its own task, being aware of others' tasks (Salas et al, 2005), and effective interaction (namely communication and coordination) with other teammates (Gorman et al, 2010;Cooke et al, 2013).…”

Section: Teaming With Autonomous Agentsmentioning

confidence: 99%

The Evolution of Human-Autonomy Teams in Remotely Piloted Aircraft Systems Operations

2019

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The focus of this current research is 2-fold: (1) to understand how team interaction in human-autonomy teams (HAT)s evolve in the Remotely Piloted Aircraft Systems (RPAS) task context, and (2) to understand how HATs respond to three types of failures (automation, autonomy, and cyber-attack) over time. We summarize the findings from three of our recent experiments regarding the team interaction within HAT over time in the dynamic context of RPAS. For the first and the second experiments, we summarize general findings related to team member interaction of a three-member team over time, by comparison of HATs with all-human teams. In the third experiment, which extends beyond the first two experiments, we investigate HAT evolution when HATs are faced with three types of failures during the task. For all three of these experiments, measures focus on team interactions and temporal dynamics consistent with the theory of interactive team cognition. We applied Joint Recurrence Quantification Analysis, to communication flow in the three experiments. One of the most interesting and significant findings from our experiments regarding team evolution is the idea of entrainment, that one team member (the pilot in our study, either agent or human) can change the communication behaviors of the other teammates over time, including coordination, and affect team performance. In the first and second studies, behavioral passiveness of the synthetic teams resulted in very stable and rigid coordination in comparison to the all-human teams that were less stable. Experimenter teams demonstrated metastable coordination (not rigid nor unstable) and performed better than rigid and unstable teams during the dynamic task. In the third experiment, metastable behavior helped teams overcome all three types of failures. These summarized findings address three potential future needs for ensuring effective HAT: (1) training of autonomous agents on the principles of teamwork, specifically understanding tasks and roles of teammates, (2) human-centered machine learning design of the synthetic agent so the agents can better understand human behavior and ultimately human needs, and (3) training of human members to communicate and coordinate with agents due to current limitations of Natural Language Processing of the agents.

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Human-Robot Teaming in Urban Search and Rescue

Cited by 25 publications

References 22 publications

Evaluating Effects of User Experience and System Transparency on Trust in Automation

Evaluating Effects of User Experience and System Transparency on Trust in Automation

Teaming With a Synthetic Teammate: Insights into Human-Autonomy Teaming

The Evolution of Human-Autonomy Teams in Remotely Piloted Aircraft Systems Operations

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