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In this talk, I will describe three threads of my research aimed at enhancing the interaction capabilities of robots, which I argue must match their technical capabilities...
Abstract: Robots are poised to become integral in our everyday lives, helping us with activities like driving and chores, but their seamless integration necessitates interacting with people effectively. In this talk, I will describe three threads of my research aimed at enhancing the interaction capabilities of robots, which I argue must match their technical capabilities such as manipulating objects. First, robots must be able to coordinate with people. To enable coordination during ad-hoc human-robot collaboration on tabletops, I introduce new explicit and implicit techniques inspired by human teaming to allow users to allocate actions to robots. Second, robots must be able to communicate with people to navigate complex social situations. To that end, I propose Generative Expressive Motion, a new method that leverages the rich social context and code-writing capabilities of large language models to generate expressive robot behaviors based on natural language instructions from humans. Lastly, users must be able to instruct robots to complete manipulation tasks based on their requirements. I present ImageInThat, which allows users to directly manipulate images representing a robot’s observations of its environment—such as repositioning objects—to create instructions the robot can later execute. I will discuss the results of evaluating these interventions with users, and conclude by outlining opportunities for further advancing the interaction capabilities of robots.
Bio: Karthik Mahadevan is a Ph.D. candidate at the University of Toronto, advised by Dr. Tovi Grossman. His research focuses on enhancing the interaction capabilities of robots so that they can excel in various contexts, from being instructed by humans in real time to coordinating with humans during collaboration. Karthik's work has been published at HCI venues such as HRI, CHI, and UIST, and has earned multiple awards, including a Best Technical Paper award at HRI 2024. During his Ph.D., he interned with research groups at Google DeepMind, Meta Reality Labs, and Autodesk. His work is supported by the Google Ph.D. Fellowship and nationally awarded NSERC CGS-D fellowship.
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