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P.: Investigating Multimodal Real-Time Patterns of Joint Attention
- in an HRI Word Learning Task. In: 5th ACM/IEEE International Conference on Human-Robot Interaction (2010
"... Abstract—Joint attention – the idea that humans make inferences from observable behaviors of other humans by attending to the objects and events that these others humans attend to – has been recognized as a critical component in human-robot interactions. While various HRI studies showed that having ..."
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Abstract—Joint attention – the idea that humans make inferences from observable behaviors of other humans by attending to the objects and events that these others humans attend to – has been recognized as a critical component in human-robot interactions. While various HRI studies showed that having robots to behave in ways that support human recognition of joint attention leads to better behavioral outcomes on the human side, there are no studies that investigate the detailed time course of interactive joint attention processes. In this paper, we present the results from an HRI study that investigates the exact time course of human multi-modal attentional processes during an HRI word learning task in an unprecedented way. Using novel data analysis techniques, we are able to demonstrate that the temporal details of human attentional behavior are critical for understanding human expectations of joint attention in HRI and that failing to do so can force humans into assuming unnatural behaviors. Keywords-human-robot interaction; joint attention I.
Affective Goal and Task Selection for Social Robots
- Handbook of Research on Synthetic Emotions and Sociable Robotics
, 2009
"... Effective decision-making under real-world conditions can be very difficult. From a purely decision-theoretic standpoint, the optimal way of making decisions – rational choice – requires an agent to know the utilities of all choice options as well as their associated likelihoods of succeeding for th ..."
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Effective decision-making under real-world conditions can be very difficult. From a purely decision-theoretic standpoint, the optimal way of making decisions – rational choice – requires an agent to know the utilities of all choice options as well as their associated likelihoods of succeeding for the agent to be able to calculate the expected utility of each alternative and being able to select the one with the maximum utility. Unfortunately, such rational methods are in practice often not applicable (e.g., because the agent does not have reliable or sufficient knowledge) or feasible (e.g., because it is too time-consuming to perform all necessary calculations). Psychologists have long hypothesized that humans are able to cope with time, knowledge and other resource limitations by employing affective evaluations (Clore, Gasper, & Conway, 2001) rather than rational ones. For affect provides fast, low-cost (although often less accurate) mecha-nisms for estimating the value of an object, event, or situation for an agent, as opposed to longer, more complex and more computationally intensive cognitive evaluations (e.g., to compute the ex-pected utilities) (Kahneman, Wakker, & Sarin, 1997). Humans also rely on affective memory, which seems to encode implicit knowledge about the likelihood of occurrence of a positive or negative future event (Blaney, 1986). Finally, affect also influences human problem-solving and
Too much humanness in human-robot interaction: Exposure to highly humanlike robots elicits aversive responding in observers
- In Proceedings of CHI
, 2015
"... People tend to anthropomorphize agents that look and/or act human, and further, they tend to evaluate such agents more positively. This, in turn, has motivated the development of robotic agents that are humanlike in appearance and/or be-havior. Yet, some agents – often those with highly humanlike ap ..."
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People tend to anthropomorphize agents that look and/or act human, and further, they tend to evaluate such agents more positively. This, in turn, has motivated the development of robotic agents that are humanlike in appearance and/or be-havior. Yet, some agents – often those with highly humanlike appearances – have been found to elicit the opposite, wherein they are evaluated more negatively than their less humanlike counterparts. These trends are captured by Masahiro Mori’s uncanny valley hypothesis, which describes a (uncanny) val-ley in emotional responding – a switch from affinity to dislike – elicited by agents that are “too humanlike”. However, while the valley phenomenon has been repeatedly observed via subjective measures, it remains unknown as to whether such evaluations reflect a potential impact to a person’s behavior (i.e., aversion). We attempt to address this gap in the literature via a novel experimental paradigm employing both traditional subjective ratings, as well as measures of peoples ’ behavioral and phsyiological respond-ing. The results show that not only do people rate highly humanlike robots as uncanny, but moreover, they exhibit greater avoidance of such encounters than encounters with less humanlike and human agents. Thus, the findings not only support Mori’s hypothesis, but further, they indicate the valley should be taken as a serious consideration for peoples’ interactions with humanlike agents. Author Keywords Human-robot interaction; uncanny valley; emotion regulation; situation selection/modification; attentional deployment; anthropomorphism; embodied conversational agents; virtual agents
IMPACT OF HUMAN LIKENESS ON ETHICAL DECISION MAKING ABOUT MEDICAL DILEMMAS
, 2009
"... ________________________________________ Karl F. MacDorman, Ph.D., Chair ________________________________________ ..."
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________________________________________ Karl F. MacDorman, Ph.D., Chair ________________________________________
The Influence of Voice Pitch on the Evaluation of a Social Robot Receptionist
"... Abstract—. In this paper we present an experiment addressing the effect of voice pitch on the evaluation of a social robot receptionist. Twenty eight test participants interacted with two “female ” robot characters: one with a high-pitched, exuberant voice, the other with a low-pitched, calm voice. ..."
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Abstract—. In this paper we present an experiment addressing the effect of voice pitch on the evaluation of a social robot receptionist. Twenty eight test participants interacted with two “female ” robot characters: one with a high-pitched, exuberant voice, the other with a low-pitched, calm voice. Our results show that the high pitch robot was perceived significantly more attractive in terms of voice, behavior and personality. We also found that the increased level of the robot’s attractiveness induced significantly better ratings on the overall enjoyment and overall interaction quality. With our study we would like to stress the importance of the voice, in general (and the voice pitch, in particular) in the social robot design and to encourage further research in this topic within the HCI (in particular HRI – Human Robot Interaction) community.
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"... Speak to me and I tell you who you are! A language-attitude study in a cultural-heritage application ..."
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Speak to me and I tell you who you are! A language-attitude study in a cultural-heritage application
Gender, more so than Age, Modulates Positive Perceptions of Language-Based Human-Robot Interactions
"... Abstract. Prior work has shown that a robot which uses polite-ness modifiers in its speech is perceived more favorably by human interactants, as compared to a robot using more direct instructions. However, the findings to-date have been based soley on data aquired from the standard university pool, ..."
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Abstract. Prior work has shown that a robot which uses polite-ness modifiers in its speech is perceived more favorably by human interactants, as compared to a robot using more direct instructions. However, the findings to-date have been based soley on data aquired from the standard university pool, which may introduce biases into the results. Moreover, the work does not take into account the po-tential modulatory effects of a person’s age and gender, despite the influence these factors exert on perceptions of both natural language interactions and social robots. Via a set of two experimental studies, the present work thus explores how prior findings translate, given a more diverse subject population recruited via Amazon’s Mechani-cal Turk. The results indicate that previous implications regarding a robot’s politeness hold even with the broader sampling. Further, they reveal several gender-based effects that warrant further attention. 1
Gentiane Venture 3
"... user study on a new Super-Wizard of Oz platform explored in a long-distance ..."
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user study on a new Super-Wizard of Oz platform explored in a long-distance
Understanding Expectations of a Robot's Identity Through Multi-User Interactions
"... We conducted two experiments looking at how to read user expectations of a robot's identity within multi-user environments. Multi-user environments are unpredictable and fast-paced, which can become a challenge for roboticists to interpret. However, they also present a rich landscape of data, a ..."
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We conducted two experiments looking at how to read user expectations of a robot's identity within multi-user environments. Multi-user environments are unpredictable and fast-paced, which can become a challenge for roboticists to interpret. However, they also present a rich landscape of data, and we propose methodologies to retrieve user reactions to the robot through sensor data. We also emphasize the necessity of using the results from these methodologies to define a robot's identity based on user expectations. In Experiment 1, we found that sensor data taken from a handshake with the robot can be used to find differences in the views of different demographic groups towards interaction with our robot. In Experiment 2, we expand the subject pool and reaffirm the usefulness of sensor data in multi-user environments, while also using questionnaire data to create an identity for our robot. I.2.9 [Robotics]: Operator interfaces – methods to research user expectations and intuitive interaction, multi-user environments, robot identity, biofeedback 1.
