Woodruff School of Mechanical Engineering
Faculty Candidate Seminar
Optimal Ergodic Control for Active Search
Dr. Lauren Miller
Automation Science Laboratory, University of California, Berkeley
Tuesday, March 1, 2016 at 11:00:00 AM
Pettit MiRC Building, Room 102 A&B
As robotic and autonomous systems become more ubiquitous and their applications more expansive, the problems we look to solve are often best characterized by desirable statistics or distributions. Automating search and exploration for mobile robots, for example, involves being able to make decisions based on distributed, probabilistic, and potentially sporadic information. I will discuss my work in developing optimal control techniques that allow such problems to be formulated directly in terms of spatial statistics using principles from ergodic theory (a trajectory’s distance from ergodicity, or its statistical distance from a distribution, can be used to define a metric suitable for optimal control). I will present experimental results using ergodic optimal control to automate active sensing tasks using a bio-inspired underwater robot, as well as future avenues of work with applications to precision agriculture, assisted surgery and rehabilitation.
Lauren Miller is a postdoctoral researcher at UC Berkeley working in the Automation Science Laboratory. She received an AB/BE in mechanical engineering from Dartmouth College in 2009, and MS and PhD degrees from Northwestern University in 2013 and 2015, respectively, as a member of the Neuroscience and Robotics Laboratory. Her research interests include robotics, optimal control, and active sensing. Lauren is also an active member in the Robotics and Automation society, and was recently chair of the student activities committee and an Administrative Committee member.