“Distinguishing Robot Personality from Motion”
In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20). Association for Computing Machinery, New York, NY, USA, 87–89
Authors: Abhijeet Agnihotri, Amy Chan, Samarendra Hedaoo, and Heather Knight.
Abstract: The central research question of this work is can robot motion effectively communicate distinct robot personalities? In this study, we implemented three distinct robot motion personalities inspired by a subset of the seven dwarfs: Happy, Sleepy, and Grumpy. We implemented autonomous motion generation systems that mapped each personality to path shape, timing, and seeking/avoidance of the participant features. A user study demonstrated that our 24 participants could distinguish these personalities. Robot motion style predicted robot personality features such as politeness, friend-liness, and intelligence, which, for the most part, matched logically to the intended dwarf personality designs. These results indicate that robot motion style is sufficient to indicate a robot’s personality during its interactive behaviors with people.
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“A Robot Barista Comments on its Clients: Social Attitudes Toward Robot Data Use”
2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Daegu, Korea (South), 2019, pp. 66-74.
Authors: Samarendra Hedaoo, Akim Williams, Chinmay Wadgaonkar, and Heather Knight
Abstract: This paper explores peoples attitudes about a service robot using customer data in conversation. In particular, how can robots understand privacy expectations in social grey-areas like cafes, which are both open to the public and used for private meetings? To answer this question, we introduce the Theater Method, which allows a participant to experience a “violation” of their privacy rather than have their actual privacy be violated. Using Python to generate 288 scripts that fully explored our research variables, we ran a large-scale online study ( N=4608). To validate our results and ask more in-depth questions, we also ran an in-person follow-up ( N=20). The experiments explored social & data-inspired variables such as data source, the positive or negative use of that data, and whom the robot verbally addressed, all of which significantly predicted participants’ social attitudes towards the robot’s politeness, consideration, appropriateness, and respect of privacy. Body language analysis and cafe-related conversation were the lowest risk, but even more extreme data channels are potentially okay when used for positive purposes.
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The Theater Method: Exploring Unethical Research Topics in Human Robot Interaction
2019, WeRobot Conference, University of Miami School of Law, April 11-13, Poster
Authors: Samarendra Hedaoo, Heather Knight
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