A friendly gesture: Investigating the effect of multimodal robot behavior in human-robot interaction (original) (raw)
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Previous work has shown that gestural behaviors affect anthropomorphic inferences about artificial communicators such as virtual agents. In an experiment with a humanoid robot, we investigated to what extent gesture would affect anthropomorphic inferences about the robot. Particularly, we examined the effects of the robot's hand and arm gestures on the attribution of typically human traits, likability of the robot, shared reality, and future contact intentions after interacting with the robot. For this, we manipulated the non-verbal behaviors of the humanoid robot in three experimental conditions: (1) no gesture, (2) congruent gesture, and (3) incongruent gesture. We hypothesized higher ratings on all dependent measures in the two gesture (vs. no gesture) conditions. The results confirm our predictions: when the robot used gestures during interaction, it was anthropomorphized more, participants perceived it as more likable, reported greater shared reality with it, and showed increased future contact intentions than when the robot gave instructions without using gestures. Surprisingly, this effect was particularly pronounced when the robot's gestures were partly incongruent with speech. These findings show that communicative non-verbal behaviors in robotic systems affect both anthropomorphic perceptions and the mental models humans form of a humanoid robot during interaction.
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International Journal of Social Robotics, 2013
Previous work has shown that non-verbal behaviors affect anthropomorphic inferences about artificial communicators such as virtual agents. In an experiment with a humanoid robot, we examined to what extent gesture affects anthropomorphic inferences made about the robot. Specifically, we investigated the effects of the robot's hand and arm gestures on the perception of humanlikeness, likability of the robot, shared reality, and future contact intentions after interacting with the robot. For this purpose, the speech-accompanying nonverbal behaviors of the humanoid robot were manipulated in three experimental conditions: (1) no gesture, (2) congruent gesture, and (3) incongruent gesture. We hypothesized higher ratings on all dependent measures in the two multimodal (speech and gesture) conditions compared to the unimodal condition (speech only). The results confirm our predictions: when the robot used coverbal gestures during interaction, it was anthropomorphized more, participants perceived it as more likable, reported greater shared reality with it, and showed increased future contact intentions than when the robot gave instructions without gestures. Surprisingly, this effect was particularly pronounced when the robot's gestures were partly incongruent with speech, although this behavior negatively affected the participants' taskrelated performance. These findings show that communicative non-verbal behaviors in robotic systems affect anthropomorphic perceptions and the mental models humans form of a humanoid robot during interaction.
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Communication between people is characterized by a broad range of nonverbal cues. Transferring these cues into the design of robots and other artificial agents that interact with people may foster more natural, inviting, and accessible experiences. In this position paper, we offer a series of definitive nonverbal codes for human-robot interaction (HRI) that address the five human sensory systems (visual, auditory, haptic, olfactory, gustatory) drawn from the field of communication studies. We discuss how these codes can be translated into design patterns for HRI using a curated sample of the communication studies and HRI literatures. As nonverbal codes are an essential mode in human communication, we argue that integrating robotic nonverbal codes in HRI will afford robots a feeling of "aliveness" or "social agency" that would otherwise be missing. We end with suggestions for research directions to stimulate work on nonverbal communication within the field of HRI and improve communication between human and robots.
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In this chapter, we are discussing the appearance and need of gestures as feedback strategy for humanoid robots in interactions with humans. Gestures are a means of communication that is nonverbal, which either supports or replaces the verbal communication, and represent a rich source of communication short cuts. We
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As robots become more prevalent in society, investigating the interactions between humans and robots is important to ensure that these robots adhere to the social norms and expectations of human users. In particular, it is important to explore exactly how the nonverbal behaviors of robots influence humans due to the dominant role nonverbal communication plays in social interactions. In this paper, we present a detailed survey on this topic focusing on four main nonverbal communication modes: kinesics, proxemics, haptics, and chronemics, as well as multimodal combinations of these modes. We uniquely investigate findings that span across these different nonverbal modes and how they influence humans in four separate ways: shifting cognitive framing, eliciting emotional responses, triggering specific behavioral responses, and improving task performance. A detailed discussion is presented to provide insights on nonverbal robot behaviors with respect to the aforementioned influence types and to discuss future research directions in this field.