A Framework for Dynamic Situational Awareness in Human–Robot Teams: An Interview Study

In human–robot teams, human situational awareness is the operator’s conscious knowledge of the team’s states, actions, plans and their environment. Appropriate human situational awareness is critical to successful human–robot collaboration. In human–robot teaming, it is often assumed that the best and required level of situational awareness is knowing everything at all times. This view is problematic, because what a human needs to know for optimal team performance varies given the dynamic environmental conditions, task context, and roles and capabilities of team members. We explore this topic by interviewing 16 participants with active and repeated experience in diverse human–robot teaming applications. Based on analysis of these interviews, we derive a framework explaining the dynamic nature of required situational awareness in human–robot teaming. In addition, we identify a range of factors affecting the dynamic nature of required and actual levels of situational awareness (i.e., dynamic situational awareness), types of situational awareness inefficiencies resulting from gaps between actual and required situational awareness, and their main consequences. We also reveal various strategies, initiated by humans and robots, that assist in maintaining the required situational awareness. Our findings inform the implementation of accurate estimates of dynamic situational awareness and the design of user-adaptive human–robot interfaces. Therefore, this work contributes to the future design of more collaborative and effective human–robot teams.

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Publication Details

Journal
ACM Transactions on Human-Robot Interaction
Published
2025-06-27
DOI
https://doi.org/10.1145/3743693
Citations
5
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
12.19
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article

A Framework for Dynamic Situational Awareness in Human–Robot Teams: An Interview Study

Hashini Senaratne, David Howard, Dana Kulić, Jason Williams et al.
5 citations
ACM Transactions on Human-Robot Interaction
Human-Automation Interaction and Safety
12.19
article

A Framework for Dynamic Situational Awareness in Human–Robot Teams: An Interview Study

Hashini Senaratne, David Howard, Dana Kulić, Jason Williams, Cécile Paris, Leimin Tian, Pavan Sikka
article en
5 citations

Abstract

In human–robot teams, human situational awareness is the operator’s conscious knowledge of the team’s states, actions, plans and their environment. Appropriate human situational awareness is critical to successful human–robot collaboration. In human–robot teaming, it is often assumed that the best and required level of situational awareness is knowing everything at all times. This view is problematic, because what a human needs to know for optimal team performance varies given the dynamic environmental conditions, task context, and roles and capabilities of team members. We explore this topic by interviewing 16 participants with active and repeated experience in diverse human–robot teaming applications. Based on analysis of these interviews, we derive a framework explaining the dynamic nature of required situational awareness in human–robot teaming. In addition, we identify a range of factors affecting the dynamic nature of required and actual levels of situational awareness (i.e., dynamic situational awareness), types of situational awareness inefficiencies resulting from gaps between actual and required situational awareness, and their main consequences. We also reveal various strategies, initiated by humans and robots, that assist in maintaining the required situational awareness. Our findings inform the implementation of accurate estimates of dynamic situational awareness and the design of user-adaptive human–robot interfaces. Therefore, this work contributes to the future design of more collaborative and effective human–robot teams.

ACM Transactions on Human-Robot InteractionVol. 15(1)
Commonwealth Scientific and Industrial Research Organisation (AU), CSIRO Manufacturing (AU), Data61 (AU), Monash University (AU)
Openalex Percentile: Top 1%
Human-Automation Interaction and Safety
12.19
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