Abstract: Contact with the outside world is challenging for robots due to its inherently discontinuous nature -- when a foot or hand is touching a surface the forces are completely different than if it is just above the surface. However, most of our computational and analytic tools for planning, learning, and control assume continuous (if not smooth or even linear) systems. Simple models of contact make assumptions (like plasticity and coulomb friction) that are known to not only be wrong physically but also inconsistent. In this talk I will present techniques for overcoming these challenges in order to adapt smooth methods to systems that have changing contact conditions. In particular I will focus on three topics: First, I will present the "Salted Kalman Filter" for state estimation over hybrid systems. Second, I will present an analysis approach that unifies and extends different strategies for stabilizing and controlling systems through contact. Finally, I will talk about when these hybrid models of contact break down, especially when driving on sand.

Bio: Aaron M. Johnson is an Associate Professor of Mechanical Engineering at Carnegie Mellon University, with additional appointments in the Robotics Institute and Electrical & Computer Engineering departments. He received his PhD from the University of Pennsylvania in 2014. His research interests are in hybrid systems, state estimation, control, legged robots, and field robotics. He is the recipient of the NSF Career award, the ARO Young Investigator Award, and the Best Paper award at the ICRA Workshop on Legged Robots, among other awards.