Evaluating Social Perception of Human-to-Robot Handovers Using the Robot Social Attributes Scale (RoSAS)

Pan, Matthew K. X. J.; Croft, Elizabeth A.; Niemeyer, G.

doi:10.1145/3171221.3171257

Cited by 36 publications

(22 citation statements)

References 2 publications

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“…Several studies involving robotic agents investigated humans’ preferences and how their perception of fluency can be influenced during the handover task. The majority of these studies focused on the effects of the trajectory (Basili et al 2009 ; Prada et al 2013 ), and of the velocity profile (Huber et al 2008a , b ) of the reaching movement; on the effects of the position of both the passer’s (Cakmak et al 2011 ; Strabala et al 2013 ; Huber et al 2013 ; Parastegari et al 2017 ) and the receiver’s arm (Pan et al 2018a , b ) used to handover the object; on the effects of other subtle non-verbal clues used to initiate an handover (Strabala et al 2012 ; Moon et al 2014 ). Only few studies focused on the modulation of the GF during the object release.…”

Section: Introductionmentioning

confidence: 99%

Humans adjust their grip force when passing an object according to the observed speed of the partner’s reaching out movement

et al. 2018

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The way an object is released by the passer to a partner is fundamental for the success of the handover and for the experienced fluency and quality of the interaction. Nonetheless, although its apparent simplicity, object handover involves a complex combination of predictive and reactive control mechanisms that were not fully investigated so far. Here, we show that passers use visual-feedback based anticipatory control to trigger the beginning of the release, to launch the appropriate motor program, and adapt such predictions to different speeds of the receiver’s reaching out movements. In particular, the passer starts releasing the object in synchrony with the collision with the receiver, regardless of the receiver’s speed, but the passer’s speed of grip force release is correlated with receiver speed. When visual feedback is removed, the beginning of the passer’s release is delayed proportionally with the receiver’s reaching out speed; however, the correlation between the passer’s peak rate of change of grip force is maintained. In a second study with 11 participants receiving an object from a robotic hand programmed to release following stereotypical biomimetic profiles, we found that handovers are experienced as more fluent when they exhibit more reactive release behaviours, shorter release durations, and shorter handover durations. The outcomes from the two studies contribute understanding of the roles of sensory input in the strategy that empower humans to perform smooth and safe handovers, and they suggest methods for programming controllers that would enable artificial hands to hand over objects with humans in an easy, natural and efficient way.

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Section: Introductionmentioning

confidence: 99%

Humans adjust their grip force when passing an object according to the observed speed of the partner’s reaching out movement

et al. 2018

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“…A study of effect of participant's previous encounters with robots on human-robot handovers showed that naive users, as opposed to experienced ones, expect the robot to monitor the handover visually, rather than merely use the force sensor [11]. A study of the impact of repeated handover experiments on the robot's social perception [12] showed that participants' emotional warmth towards the robot and comfort were improved by repeated experiments.…”

Section: Human-to-robot Handoversmentioning

confidence: 99%

Human Preferences for Robot Eye Gaze in Human-to-Robot Handovers

Faibish

Kshirsagar

Hoffman

et al. 2022

Int J of Soc Robotics

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This paper investigates human's preferences for a robot's eye gaze behavior during human-to-robot handovers. We studied gaze patterns for all three phases of the handover process: reach, transfer, and retreat, as opposed to previous work which only focused on the reaching phase. Additionally, we investigated whether the object's size or fragility or the human's posture affect the human's preferences for the robot gaze. A public data-set of human-human handovers was analyzed to obtain the most frequent gaze behaviors that human receivers perform. These were then used to program the robot's receiver gaze behaviors. In two sets of user studies (video and in-person), a collaborative robot exhibited these gaze behaviors while receiving an object from a human. In the video studies, 72 participants watched and compared videos of handovers between a human actor and a robot demonstrating each of the three gaze behaviors. In the in-person studies, a different set of 72 participants physically performed object handovers with the robot and evaluated their perception of the handovers for the robot's different gaze behaviors. Results showed that, for both observers and participants in a handover, when the robot exhibited Face-Hand-Face gaze (gazing at the giver's face and then at the giver's hand during the reach phase and back at the giver's face during the retreat phase), participants considered the handover to be more likable, anthropomorphic, and communicative of timing ( p < 0.0001). However, we did not find evidence of any effect of the object's size or fragility or the giver's posture on the gaze preference. KeywordsHuman-robot handovers • Human-robot interaction • Robot eye gaze • Human-human-handovers • Non-verbal communication Tair Faibish and Alap Kshirsagar contributed equally to this work.

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“…There were studies that used commercialized robots (e.g., Pepper, iCat, Karotz, Aibo, RoboVie) for their research [17][18][19][20][21][22][23][24]. When fully commercialized robots can be provided, test participants can interact with the robot freely.…”

Section: Type Of Stimulimentioning

confidence: 99%

“…Five subjective evaluation tools that were used in the articles reviewed are summarized in Table 3. [4] negative attitude toward situations of interaction with robots, negative attitude toward social influence of robots, negative attitude toward emotions in interaction with robots Godspeedscale [5] anthropomorphism, animacy, likeability, perceived intelligence, perceived safety RoSAS(Robot Social Attributes Scale) [19] competence, warmth, discomfort…”

Section: Referred Evaluation Tools Of Subjective Measuresmentioning

confidence: 99%

Evaluation of Methodologies and Measures on the Usability of Social Robots: A Systematic Review

2021

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This paper systemically reviews and clarifies the state-of-the-art HRI evaluation studies especially for the usability of social robots. A total of 36 articles were collected through a keyword, abstract, title search from various online search engines. Afterwards, 163 measures were selected and reviewed carefully. This research was classified into two parts. In the first part, evaluation methodologies were investigated according to (1) type of stimuli on evaluation, (2) evaluation technique, and (3) criteria of participants. In the second part, assessment measures were collected and the model of attitude towards a social robot is proposed. As a result, this study suggests practical strategies for selecting appropriate methods and measures that meet specific requirements of research. The proposed hierarchical structure of assessment measures is expected to contribute to both practical use and academic use.

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Evaluating Social Perception of Human-to-Robot Handovers Using the Robot Social Attributes Scale (RoSAS)

Cited by 36 publications

References 2 publications

Humans adjust their grip force when passing an object according to the observed speed of the partner’s reaching out movement

Humans adjust their grip force when passing an object according to the observed speed of the partner’s reaching out movement

Human Preferences for Robot Eye Gaze in Human-to-Robot Handovers

Evaluation of Methodologies and Measures on the Usability of Social Robots: A Systematic Review

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