The importance of shared mental models and shared situation awareness for transforming robots from tools to teammates

Ososky, Scott; Schuster, David M.; Jentsch, Florian; Fiore, Stephen M.; Shumaker, Randall; Lebière, Christian; Kurup, Unmesh; Oh, Jean; Stentz, Anthony

doi:10.1117/12.923283

Cited by 33 publications

(23 citation statements)

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“…The TRADR userinterface system is aimed at improving joint situation awareness, thus promoting the quick propagation of information to relevant members of the team. Important in this aspect is the establishment of shared mental models [5], to ensure a common understanding of the state of the world (realized through the ontology), the role of the team and of individual members.…”

Section: Human Aspectsmentioning

confidence: 99%

Human-Robot Teamwork in USAR Environments

Greeff

Hindriks

Neerincx

et al. 2015

Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts

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The TRADR project aims at developing methods and models for human-robot teamwork, enabling robots to operate in search & rescue environments alongside humans as teammates, rather than as tools. Through a user-centered cognitive engineering method, human-robot teamwork is analyzed, modeled, implemented and evaluated in an iterative fashion. Important is the notion of persistence: rather than treating each sortie as a separate instance for which the build-up of situation awareness and exploration starts from scratch, the objective for the TRADR project is to provide robotic support in an ongoing, fluent manner. This paper provides a short overview of important aspects for humanrobot teaming, such as human-robot teamwork coordination and joint situation awareness.

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Section: Human Aspectsmentioning

confidence: 99%

Human-Robot Teamwork in USAR Environments

Greeff

Hindriks

Neerincx

et al. 2015

Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts

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“…While individual SA helps individual agents achieve goals, shared SA is also beneficial, because it is a cornerstone in allowing for coordination among agents, including both humans and automation [23]. Shared SA, based on common ground [8], helps ensure that the members of the distributed network as a whole can smoothly and synergistically react to the same infonnation with the same conclusions [23].…”

Section: ) Shared and Complementary Samentioning

confidence: 99%

“…Shared SA, based on common ground [8], helps ensure that the members of the distributed network as a whole can smoothly and synergistically react to the same infonnation with the same conclusions [23]. Research has demonstrated that observing shared SA in addition to individual SA increases understanding of the overall effectiveness of teams' mental models [23]; that shared SA improves action coordination through team members' anticipation of infonnation needed to support team activities [21]; that shared SA increases efficiency and safety [28]; and that it is an important variable for predicting team performance [4].…”

Section: ) Shared and Complementary Samentioning

confidence: 99%

“…Being separate from most [9,27] previous researchers' interpretation of shared SA, complementary SA fills a gap in the measurement of SA. It takes into account situations in which team members need to have different knowledge and sub-tasks in addition to needing to communicate and hold common ground [23]. It allows agents to contribute to common goals like pieces of a puzzle, simultaneously completing independent projects to benefit overall SA without increasing the workload of other agents.…”

Section: Introductionmentioning

confidence: 99%

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Applying measurement to complementary situation awareness

Cain

Schuster

2016

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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As networks in complex domains such as cyber security increasingly become distributed, with multiple human and automated agents working together to complete team goals, capturing situation awareness (SA) becomes more difficult.Often, SA is defined and measured as individual SA (the knowledge held by an individual, such as a system administrator) or as shared SA (the knowledge held in common by multiple individuals). For these two types, ideal and actual SA have been measured using goal-oriented task analysis and knowledge specific queries, respectively. We argue that measurements of SA could fill a gap by additionally measuring complementary SA (the knowledge elements held separately by individuals). In the current paper, we suggest how measures for individual SA can be applied to the measurement of the complementary component of SA. We adapt a technique that involves completing a goal oriented task analysis for a given context and then querying human operators about specific knowledge elements. This adaption allows for the quantification of goal-oriented knowledge elements that are held by team members but are not shared. This technique for quantifying team SA that is complementary as well as shared can be applied to assess trainees and to inform future training programs. Understanding and measuring multiple facets of SA will help improve efficiency and security in distributed teams in cyber security. First, we review the literature on existing measurement techniques for SA, then we outline how measurement can be applied to complementary SA. Lastly, we discuss some applications of measuring complementary SA.

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“…In order for the U.S. Army to adopt Human-Robot Teams (HRT), significant advances are needed in robot automation, capabilities, and interfaces. Currently, most UGV platforms supporting military ISR missions utilize some form of teleoperation through either a joystick, track ball, or touch screen 3 However, as the level of autonomy increases in robots through advances in perception, obstacle avoidance, and navigation algorithms, they are transitioning from tools to teammates 4 . Incorporating robots into existing human ISR teams presents a Human-Robot Interaction (HRI) challenge.…”

Section: Introductionmentioning

confidence: 99%

Speech and gesture interfaces for squad-level human-robot teaming

Harris

Barber

2014

SPIE Proceedings

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As the military increasingly adopts semi-autonomous unmanned systems for military operations, utilizing redundant and intuitive interfaces for communication between Soldiers and robots is vital to mission success. Currently, Soldiers use a common lexicon to verbally and visually communicate maneuvers between teammates. In order for robots to be seamlessly integrated within mixed-initiative teams, they must be able to understand this lexicon. Recent innovations in gaming platforms have led to advancements in speech and gesture recognition technologies, but the reliability of these technologies for enabling communication in human robot teaming is unclear. The purpose for the present study is to investigate the performance of Commercial-Off-The-Shelf (COTS) speech and gesture recognition tools in classifying a Squad Level Vocabulary (SLV) for a spatial navigation reconnaissance and surveillance task. The SLV for this study was based on findings from a survey conducted with Soldiers at Fort Benning, GA. The items of the survey focused on the communication between the Soldier and the robot, specifically in regards to verbally instructing them to execute reconnaissance and surveillance tasks. Resulting commands, identified from the survey, were then converted to equivalent arm and hand gestures, leveraging existing visual signals (e.g. U.S. Army Field Manual for Visual Signaling). A study was then run to test the ability of commercially available automated speech recognition technologies and a gesture recognition glove to classify these commands in a simulated intelligence, surveillance, and reconnaissance task. This paper presents classification accuracy of these devices for both speech and gesture modalities independently.

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The importance of shared mental models and shared situation awareness for transforming robots from tools to teammates

Cited by 33 publications

References 0 publications

Human-Robot Teamwork in USAR Environments

Human-Robot Teamwork in USAR Environments

Applying measurement to complementary situation awareness

Speech and gesture interfaces for squad-level human-robot teaming

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