How anthropomorphism affects empathy toward robots

Riek, Laurel D.; Rabinowitch, Tal-Chen; Chakrabarti, Bhismadev; Robinson, Peter

doi:10.1145/1514095.1514158

Cited by 235 publications

(186 citation statements)

References 7 publications

(6 reference statements)

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“…Second, using humanlike primitives for controlling robotic systems could improve the effectiveness and safety of human-robot interaction (HRI). Indeed, several studies identified anthropomorphism as one of the key enabling factor for successful, acceptable, predictable, and safe HRI in many fields, such as human robot co-working and rehabilitative/assistive robotics (Duffy, 2003;Bartneck et al, 2009;Riek et al, 2009;Dragan and Srinivasa, 2014). Furthermore, the here reported experimental and analytical framework could be used to identify principal actuation schemes for under-actuated robotic devices.…”

Section: Conclusion and Implications For Robotics And Bioengineeringmentioning

confidence: 87%

Unvealing the Principal Modes of Human Upper Limb Movements through Functional Analysis

et al. 2017

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The rich variety of human upper limb movements requires an extraordinary coordination of different joints according to specific spatio-temporal patterns. However, unvealing these motor schemes is a challenging task. Principal components have been often used for analogous purposes, but such an approach relies on hypothesis of temporal uncorrelation of upper limb poses in time. To overcome these limitations, in this work, we leverage on functional principal component analysis (fPCA). We carried out experiments with 7 subjects performing a set of most significant human actions, selected considering state-of-the-art grasp taxonomies and human kinematic workspace. fPCA results show that human upper limb trajectories can be reconstructed by a linear combination of few principal time-dependent functions, with a first component alone explaining around 60/70% of the observed behaviors. This allows to infer that in daily living activities humans reduce the complexity of movement by modulating their motions through a reduced set of few principal patterns. Finally, we discuss how this approach could be profitably applied in robotics and bioengineering, opening fascinating perspectives to advance the state of the art of artificial systems, as it was the case of hand synergies.

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Section: Conclusion and Implications For Robotics And Bioengineeringmentioning

confidence: 87%

Unvealing the Principal Modes of Human Upper Limb Movements through Functional Analysis

et al. 2017

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“…It has been shown that individuals show more empathy toward more anthropomorphic robots (Riek et al, 2009;Darling, 2015). Convincing simulation of humanlike hand and eye movements may induce implicit impressions of animacy and anthropomorphism, potentially attributing greater value and pleasure to robotic art through empathic responses (Bartneck et al, 2009).…”

Section: The Importance Of Embodiment In Artmentioning

confidence: 99%

Putting the art in artificial: Aesthetic responses to computer-generated art.

Chamberlain¹,

Mullin²,

Scheerlinck³

et al. 2018

Psychology of Aesthetics, Creativity, and the Arts

103

116

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As artificial intelligence (AI) technology increasingly becomes a feature of everyday life, it is important to understand how creative acts, regarded as uniquely human, can be valued if produced by a machine. The current studies sought to investigate how observers respond to works of visual art created either by humans or by computers. Study 1 tested observers' ability to discriminate between computer-generated and man-made art, and then examined how categorisation of art works impacted on perceived aesthetic value, revealing a bias against computer-generated art. In Study 2 this bias was reproduced in the context of robotic art, however it was found to be reversed when observers were given the opportunity to see robotic artists in action. These findings reveal an explicit prejudice against computergenerated art, driven largely by the kind of art observers believe computer algorithms are capable of producing. These prejudices can be overridden in circumstances in which observers are able to infer anthropomorphic characteristics in the computer programs, a finding which has implications for the future of artistic AI.

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“…In our previous work, we sought to understand how people could empathize with robots of varying degrees of humanlikeness [12], [13]. However, as we discovered during the course of our research, this problem is multi-faceted.…”

Section: Introductionmentioning

confidence: 99%