Control for Information Acquisition

Wednesdays@NICO | 12:00-1:00 PM, February 17, 2016 | Chambers Hall, Lower Level

Todd Murphey, Associate Professor of Mechanical Engineering, McCormick School of Engineering, Northwestern University

Abstract

Autonomous active sensing creates the need for sensor motion to maximize information acquisition. Synthesizing trajectories for nonlinear dynamical systems generally requires some form of metric that allows one to compute the utility of a trajectory, including both its information value and its energetic cost. In this talk, I will discuss the use of information-based metrics, such as ergodicity, to define the quality of a trajectory. Optimizing these metrics can be both mathematically and numerically challenging, but using them as part of a trajectory optimization scheme allows one to directly encode information needs into the continuous-time evolution of a dynamic system. I will illustrate the ideas with a variety of experimental and computational results, including using an underwater robot in localization tasks and a Baxter robot automatically experimenting on its environment.

Bio

Dr. Todd D. Murphey is an Associate Professor of Mechanical Engineering at Northwestern University. He received his B.S. degree in mathematics from the University of Arizona and the Ph.D. degree in Control and Dynamical Systems from the California Institute of Technology. His laboratory is part of the Neuroscience and Robotics Laboratory, and his research interests include computational methods for mechanics and optimal control, physical networks, and information theory in physical systems. Honors include the National Science Foundation CAREER award in 2006, membership in the 2014-2015 DARPA/IDA Defense Science Study Group, and Northwestern's Charles Deering McCormick Professorship of Teaching Excellence. He is a Senior Editor of the IEEE Transactions on Robotics.

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