Research Interests
Social Robot Design
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The physical design of a robot shapes how people expect to interact with it. In this area, I have formed a dataset of social robot embodiments and their social and functional expectations that can be used to inform the design of these robots. I have also shown how clothing design and context affect how people gender robots. I have also helped conduct studies that examine how queer users can be uniquely harmed by AI systems if they are not considered in the design process and how gender-neutral voice use is perceived in robots.
Assistive Systems and Algorithms
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Because robots are physically embodied they can provide an interface to interact with environments that are not design to accomodate differences in mobility. In this area, I have developed a system for combing hair with a robot that can generate brush strokes from a single click. I have also developed a system to assess nonuse in poststroke participants to gather information that can help with personalized rehabilitation.
Learning Human Preferences
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Robots have to work in complex social and physical contexts that cannot be anticipated before deployment. To adapt to varied contexts, I have developed a tool to allow end-users to program robot signaling behaviors. I have also shown that user preferences for robot feedback can be clustered from observational data and can help keep participants with cerebral palsy engaged in practicing orthosis use.
