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Abstract: The goal of my research is to enable robots to autonomously produce behavior that reasons about function _and_ interaction with and around people. I aim to develop a formal understanding of interaction that leads to algorithms which are informed by mathematical models of how people interact with robots, enabling generalization across robot morphologies and interaction modalities. In this talk, I will focus on one specific instance of this agenda: autonomously generating motion for coordination during human-robot collaborative manipulation. Most motion in robotics is purely functional: industrial robots move to package parts, vacuuming robots move to suck dust, and personal robots move to clean up a dirty table. This type of motion is ideal when the robot is performing a task in isolation. Collaboration, however, does not happen in isolation, and demands that we move beyond purely functional motion. In collaboration, the robot's motion has an observer, watching and interpreting the motion – inferring the robot's intent from the motion, and anticipating the robot's motion based on its intent. My work develops a mathematical model of these inferences, and integrates this model into motion planning, so that the robot can generate motion that matches people's expectations and clearly conveys its intent. In doing so, I draw on action interpretation theory, Bayesian inference, constrained trajectory optimization, and interactive learning. The resulting motion not only leads to more efficient collaboration, but also increases the fluency of the interaction as defined through both objective and subjective measures. The underlying formalism has been applied across robot morphologies, from manipulator arms to mobile robots, and across interaction modalities, such as motion, gestures, and shared autonomy with assistive arms.

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