by Dr. Dylan Losey, Postdoctoral Scholar in Computer Science at Stanford University
Every person is different: we cook different foods, arrange different homes, and have different capabilities. Robots that collaborate with humans should embrace our differences and personalize their behavior. In this talk, I will describe my works towards a formalism for personalizing human-robot interaction. I will focus on three challenges: (a) enabling robots to safely work alongside humans, (b) learning from the resulting interactions to adapt to the human’s preferences, and (c) understanding how robots can leverage these interactions to influence the user. I am particularly excited to describe how my formalism applies to assistive robotics, where robots must coordinate with humans that have various levels of impairment, degrees of mobility, and desired goals.
Dylan Losey is a postdoctoral scholar in Computer Science at Stanford University. His research interests lie at the intersection of human-robot interaction, learning from humans, and control theory. Specifically, he works on developing algorithms that enable robots to personalize their behavior and collaborate with human partners. Dylan received his Ph.D. in Mechanical Engineering from Rice University in 2018, his M.S. in Mechanical Engineering from Rice University in 2016, and his B.E. in Mechanical Engineering from Vanderbilt University in 2014. During the summer of 2017, Dylan was also a visiting scholar at the University of California, Berkeley. He has been awarded the IEEE/ASME Transactions on Mechatronics Best Paper Award, the Outstanding Ph.D. Thesis Award from the Rice University Department of Mechanical Engineering, and an NSF Graduate Research Fellowship.