You Are In My Way: Non-verbal Social Cues for Legible Robot Navigation Behaviors

Angelopoulos, Georgios; Rossi, Alessandra; Napoli, Claudia Di; Rossi, Silvia

doi:10.1109/iros47612.2022.9981754

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…In the light of our results, an arrow would be more appropriate than a gaze display if response efficiency, i.e., the speed of the evasive movement against the cued direction, is to be optimized. In the context of this scenario, our results are consistent with those of Angelopoulos and colleagues [24], who showed that deictic gesture cues can outperform gaze cues in communicating intended navigational behavior. However, when a robot is approaching a human directly, making eye contact with the human is perceived as more comfortable than looking in the direction of its path [39].…”

Section: Plos Onesupporting

confidence: 91%

“…Because artificial agents can have many different appearances, simple signs as arrows and schematic faces are still of interest for guiding visual attention with displays. In context of approaching behavior of robots, there are already studies on lights and arrows (e.g.,[ 23 ]), but also on eyes and gestures as nonverbal cues (e.g., [ 24 ]).…”

Section: Introductionmentioning

confidence: 99%

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Attentional cueing: Gaze is harder to override than arrows

Schmitz,

Strauss,

Reinel

et al. 2024

PLoS ONE

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Gaze is an important and potent social cue to direct others’ attention towards specific locations. However, in many situations, directional symbols, like arrows, fulfill a similar purpose. Motivated by the overarching question how artificial systems can effectively communicate directional information, we conducted two cueing experiments. In both experiments, participants were asked to identify peripheral targets appearing on the screen and respond to them as quickly as possible by a button press. Prior to the appearance of the target, a cue was presented in the center of the screen. In Experiment 1, cues were either faces or arrows that gazed or pointed in one direction, but were non-predictive of the target location. Consistent with earlier studies, we found a reaction time benefit for the side the arrow or the gaze was directed to. Extending beyond earlier research, we found that this effect was indistinguishable between the vertical and the horizontal axis and between faces and arrows. In Experiment 2, we used 100% “counter-predictive” cues; that is, the target always occurred on the side opposite to the direction of gaze or arrow. With cues without inherent directional meaning (color), we controlled for general learning effects. Despite the close quantitative match between non-predictive gaze and non-predictive arrow cues observed in Experiment 1, the reaction-time benefit for counter-predictive arrows over neutral cues is more robust than the corresponding benefit for counter-predictive gaze. This suggests that–if matched for efficacy towards their inherent direction–gaze cues are harder to override or reinterpret than arrows. This difference can be of practical relevance, for example, when designing cues in the context of human-machine interaction.

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Section: Plos Onesupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Attentional cueing: Gaze is harder to override than arrows

Schmitz,

Strauss,

Reinel

et al. 2024

PLoS ONE

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“…In this work, we focus on inferring three robot performance dimensions relevant to navigation [12]: robot competence, the surprisingness of robot behavior, and clear intent. Robot competence is a popular performance metric [21], especially in robot navigation [22]- [24]. Surprising behavior violates expectations.…”

Section: Related Workmentioning

confidence: 99%

Recent Advances in Mobile Robot Localization in Complex Scenarios

Zhang

Liu

et al. 2023

Lecture Notes in Electrical Engineering

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Human impressions of robot performance are often measured through surveys. As a more scalable and cost-effective alternative, we study the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques. To this end, we first contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in a Virtual Reality simulation, together with impressions of robot performance provided by users on a 5-point scale. Second, we contribute analyses of how well humans and supervised learning techniques can predict perceived robot performance based on different combinations of observation types (e.g., facial, spatial, and map features). Our results show that facial expressions alone provide useful information about human impressions of robot performance; but in the navigation scenarios we tested, spatial features are the most critical piece of information for this inference task. Also, when evaluating results as binary classification (rather than multiclass classification), the F1-Score of human predictions and machine learning models more than doubles, showing that both are better at telling the directionality of robot performance than predicting exact performance ratings. Based on our findings, we provide guidelines for implementing these predictions models in real-world navigation scenarios.

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“…Robotic communication with people has been studied for different robot types and in different public scenarios. Angelopoulos et al [29] provide an excellent review of prior work on non-verbal communication for both humanoid and non-human robots in public. Their review of prior studies found that LED light strips may not be entirely interpretable without prior training on what the signals mean [30] and that gaze may be a more understandable signal in communicating a robot's trajectory [31].…”

Section: Human-robot Communication In Publicmentioning

confidence: 99%

Sharing the Sidewalk: Observing Delivery Robot Interactions with Pedestrians during a Pilot in Pittsburgh, PA

Weinberg

Dwyer

Fox

et al. 2023

MTI

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Sidewalk delivery robots are being deployed as a form of last-mile delivery. While many such robots have been deployed on college campuses, fewer have been piloted on public sidewalks. Furthermore, there have been few observational studies of robots and their interactions with pedestrians. To better understand how sidewalk robots might integrate into public spaces, the City of Pittsburgh, Pennsylvania conducted a pilot of sidewalk delivery robots to understand possible uses and the challenges that could arise in interacting with people in the city. Our team conducted ethnographic observations and intercept interviews to understand how residents perceived of and interacted with sidewalk delivery robots over the course of the public pilot. We found that people with limited knowledge about the robots crafted stories about their purpose and function. We observed the robots causing distractions and obstructions with different sidewalk users (including children and dogs), witnessed people helping immobilized robots, and learned about potential accessibility issues that the robots may pose. Based on our findings, we contribute a set of recommendations for future pilots, as well as questions to guide future design for robots in public spaces.

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You Are In My Way: Non-verbal Social Cues for Legible Robot Navigation Behaviors

Cited by 14 publications

References 28 publications

Attentional cueing: Gaze is harder to override than arrows

Attentional cueing: Gaze is harder to override than arrows

Recent Advances in Mobile Robot Localization in Complex Scenarios

Sharing the Sidewalk: Observing Delivery Robot Interactions with Pedestrians during a Pilot in Pittsburgh, PA

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