Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach

Goodrich, Michael A.; Yi, Daqing

doi:10.1007/978-3-642-39405-8_30

Cited by 15 publications

(8 citation statements)

References 13 publications

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“…and they help people to describe, explain and predict events in their environment." Other work (Goodrich & Yi, 2013;Kiesler & Goetz, 2002;Nikolaidis & Shah, 2013) has shown the effect of shared mental models on team performance for human-robot teams, as well. Using these insights, we propose a way for the robot to communicate its internal state to the human.…”

Section: Communication Of Robot Internal Statementioning

confidence: 98%

Planning with Verbal Communication for Human-Robot Collaboration

Nikolaidis

Kwon

Forlizzi

et al. 2018

J. Hum.-Robot Interact.

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Human collaborators coordinate effectively their actions through both verbal and non-verbal communication. We believe that the the same should hold for human-robot teams. We propose a formalism that enables a robot to decide optimally between doing a task and issuing an utterance. We focus on two types of utterances: verbal commands, where the robot expresses how it wants its human teammate to behave, and state-conveying actions, where the robot explains why it is behaving this way. Human subject experiments show that enabling the robot to issue verbal commands is the most effective form of communicating objectives, while retaining user trust in the robot. Communicating why information should be done judiciously, since many participants questioned the truthfulness of the robot statements.

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Section: Communication Of Robot Internal Statementioning

confidence: 98%

Planning with Verbal Communication for Human-Robot Collaboration

Nikolaidis

Kwon

Forlizzi

et al. 2018

J. Hum.-Robot Interact.

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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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“…As SMMs have been implicated in successful human teamwork, it is theorized that they can facilitate interaction and coordination in mixed human-agent teams as well. Several studies have shown promising results in software systems [12,18] and more recently in robots [19][20][21]. However, these studies often focused on a single aspect of the SMM (e.g., predicting workload [22]) and were not grounded in a comprehensive formal or computational framework.…”

Section: Shared Mental Modelsmentioning

confidence: 99%

Shared Mental Models to Support Distributed Human-Robot Teaming in Space

Gervits

Fong

Scheutz

2018

2018 AIAA SPACE and Astronautics Forum and Exposition

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Effective human-robot interaction (HRI) is a critical requirement for current and future space operations. However, given the limitations of autonomous technologies, robots are not yet capable of coordinating with human crew as peers under real-world mission constraints. Due to the complexity inherent in space robotics operations, it is crucial that robots are able to coordinate the various aspects of human-robot teaming and the task at hand. In this paper, we extend our Shared Mental Model (SMM) interaction framework to show how it can be used to overcome some of the challenges inherent in distributed HRI and facilitate coordination in space robotics teams. Since this framework has not yet been implemented in real systems, we conducted an exploratory study to identify potential benefits that the SMM mechanisms can afford in a task domain involving simulated free-flying robot assistants on a spacecraft. We found that the SMM framework offers advantages to task performance and team efficiency due to its support of shared knowledge representations, however these benefits do not seem to reduce workload or improve other subjective measures. The significance of these findings for future space operations is discussed, as are directions for future research. * Note that true independence may not even be considered interaction by some definitions. We are using the term to refer to tasks in which the interdependent human and robot actions are separated in time.

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Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach

Cited by 15 publications

References 13 publications

Planning with Verbal Communication for Human-Robot Collaboration

Planning with Verbal Communication for Human-Robot Collaboration

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

Shared Mental Models to Support Distributed Human-Robot Teaming in Space

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