Human-agent teaming for robot management in multitasking environments

Chen, Jessie Y. C.; Quinn, Stephanie A.; Wright, Julia L.; Barnes, Michael; Barber, Daniel; Adams, David

doi:10.1109/hri.2013.6483522

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…A second theme was that different levels of agent autonomy may help some individuals more than others. Participants with low spatial ability experienced the greatest performance benefits from increases in agent autonomy (Chen et al, 2013; Wright et al, 2013, 2018), as did participants with the highest attentional control (Wright et al, 2013, 2018). However, participants with high spatial ability experienced decreases in situational awareness from increases in agent autonomy.…”

Section: Results Of Literature Review: Research Environments Themes A...mentioning

confidence: 99%

“…In 11 of the studies, the role of higher agent autonomy involved performing more activities or tasks. For example, with increasing agent autonomy in the MIX testbed, the autonomous agent takes on more and more workload, such that under partial agent autonomy it maintains vehicle distance and separation only, whereas under high agent autonomy it also provides route planning advice (Chen et al, 2013). In four of the studies, the higher levels of agent autonomy involved more decision-making and information exchange.…”

Section: Results Of Literature Review: Research Environments Themes A...mentioning

confidence: 99%

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Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

et al. 2020

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Objective We define human–autonomy teaming and offer a synthesis of the existing empirical research on the topic. Specifically, we identify the research environments, dependent variables, themes representing the key findings, and critical future research directions. Background Whereas a burgeoning literature on high-performance teamwork identifies the factors critical to success, much less is known about how human–autonomy teams (HATs) achieve success. Human–autonomy teamwork involves humans working interdependently toward a common goal along with autonomous agents. Autonomous agents involve a degree of self-government and self-directed behavior (agency), and autonomous agents take on a unique role or set of tasks and work interdependently with human team members to achieve a shared objective. Method We searched the literature on human–autonomy teaming. To meet our criteria for inclusion, the paper needed to involve empirical research and meet our definition of human–autonomy teaming. We found 76 articles that met our criteria for inclusion. Results We report on research environments and we find that the key independent variables involve autonomous agent characteristics, team composition, task characteristics, human individual differences, training, and communication. We identify themes for each of these and discuss the future research needs. Conclusion There are areas where research findings are clear and consistent, but there are many opportunities for future research. Particularly important will be research that identifies mechanisms linking team input to team output variables.

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Section: Results Of Literature Review: Research Environments Themes A...mentioning

confidence: 99%

Section: Results Of Literature Review: Research Environments Themes A...mentioning

confidence: 99%

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

et al. 2020

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“…MBC and MBE LOAs correspond to function allocations in which automation aids in the "implementation aspects of the task," the most favorable allocation found by Kaber and Endsley (1997). A common finding is that the effects of automation of one task are frequently observed in performance on another (Chen et al, 2013;Chien et al, 2011). Automating navigation, for example, can improve processing of targets, so measures of performance need to balance relative losses and gains in order to judge effectiveness.…”

Section: Loas In Loosely Coupled Multi-uv Systemsmentioning

confidence: 99%

Human Interaction With Multiple Remote Robots

Lewis¹

2013

Reviews of Human Factors and Ergonomics

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In this chapter, I review research involving remote human supervision of multiple unmanned vehicles (UVs) using command complexity as an organizing construct. Multi-UV tasks range from foraging, requiring little coordination among UVs, to formation following, in which UVs must function as a cohesive unit. Command complexity, the degree to which operator effort increases with the number of supervised UVs, is used to categorize human interaction with multiple UVs. For systems in which each UV requires the same form of attention (O(n)), effort increases linearly with the number of UVs. For systems in which the control of one UV is dependent upon another (O(>n)), additional UVs impose greater than linear increases due to the expense of coordination. For other systems, an operator interacts with an autonomously coordinating group, and effort is unaffected by group size (O(1)). Studies of human/multi-UV interaction can be roughly grouped into O(n) supervision, involving one-to-one control of individual UVs, or O(1) commanding, in which higherlevel commands are directed to a group. Research in O(n) command has centered on roundrobin control, neglect tolerance, and attention switching. Approaches to O(1) command are divided into systems using autonomous path planning only, plan libraries, human-steered planners, and swarms. Each type of system has its advantages. Less complete work in scalable displays for multiple UVs is reviewed. Mixing levels of command is probably necessary to supervise multiple UVs performing realistic tasks. Research in O(n) control is mature and can provide quantitative and qualitative guidance for design. Interaction with planners and swarms is less mature but more critical to developing effective multi-UV systems capable of performing complex tasks. IntRoductIon In recent decades, the number of mobile unmanned vehicles (UVs) deployed in field applications has risen dramatically. Their usage offers obvious advantages of reduced costs, removing humans from harm's way, or enabling entirely new applications that were previously impossible, especially when combining many such UVs into a comprehensive system. The domains of potential application are equally diverse and range from low-cost warehouse security to interplanetary exploration. New developments in commodity hardware, which serve as low-cost replacements for otherwise expensive sensing or motion capabilities, promise to further accelerate the trend toward deploying large teams of mobile UVs. This trend, however, poses a challenge for the control of such systems.

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“…First, a design of interface and controller that facilitates communication through high visibility of affordances of the input device, sufficient feedback about the current status of the robot, and adaptable and autonomous controllers generally benefits successful HRI. The studies included in this meta-analysis provide detailed examples for the practical application of different forms of highly visible affordances (e.g., Radmard et al, 2015; Walker et al, 2015; Will et al, 2013), feedback (e.g., Harder et al, 2016; Hou & Mahony, 2016; Zhang & Adams, 2012), possibilities to design adaptable controllers (e.g., Gopinathan et al, 2017; Hoffman & Breazeal, 2007; Huang & Mutlu, 2016), and different degrees of autonomy (e.g., Chen et al, 2013; Dawson et al, 2015; Geiskkovitch et al, 2015). Robot designers might use this overview as a starting point for designing robots that foster successful HRI through well-designed communication.…”

Section: Discussionmentioning

confidence: 99%

Let’s Work Together: A Meta-Analysis on Robot Design Features That Enable Successful Human–Robot Interaction at Work

et al. 2020

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Objective This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human–robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness). Background Robots are becoming an integral part of many workplaces. As interactions with employees increase, ensuring success becomes ever more vital. Even though many studies investigated robot design features, an overview on general and specific effects is missing. Method Systematic selection of literature and structured coding led to 81 included experimental studies containing 380 effect sizes. Mean effects were calculated using a three-level meta-analysis to handle dependencies of multiple effect sizes in one study. Results Sufficient feedback through the interface, clear visibility of affordances, and adaptability and autonomy of the controller significantly affect successful HRI, whereas appearance does not. The features of the interface and controller affect performance and satisfaction but do not affect situation awareness and trust. Specific effects of adaptability on cooperation and acceptance, as well as autonomy on mental workload, could be shown. Conclusion Robot design at work needs to cover multiple features of interface and controller to achieve successful HRI that covers not only performance and satisfaction, but also cooperation, acceptance, and mental workload. More empirical research is needed to investigate mediating mechanisms and underrepresented design features’ effects. Application Robot designers should carefully choose design features to balance specific effects and implementation costs with regard to tasks, work design aims, and employee needs in the specific work context.

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Human-agent teaming for robot management in multitasking environments

Cited by 9 publications

References 6 publications

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

Human Interaction With Multiple Remote Robots

Let’s Work Together: A Meta-Analysis on Robot Design Features That Enable Successful Human–Robot Interaction at Work

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