The Institute for Robotics and Intelligent Machines presents "Robots with Privacy Stipulations" by Dylan Shell of Texas A&M University. The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 12:15-1:15 p.m. and is open to the public.

Abstract

In late July last year, it came to light that iRobot Corp. intended to sell the maps that modern Roomba vacuum cleaning robots build to help them navigate. This caused a public furor among consumers. This situation and several others (e.g., nuclear inspection, use of untrusted cloud computing infrastructure) suggest that we might be interested in limiting what information a robot might divulge. How should we think about robotic privacy? In this talk I’ll describe a line of research that is concerned with this question, starting by showing that cryptography doesn’t solve the problem. I’ll begin by examining a privacy-preserving tracking task, then look at how one might think about estimators that are constrained to ensure they never know too much. Finally, I’ll talk about planning subject to information disclosure constraints and introduce a useful structure that we call a “plan closure.”

Bio

Dylan Shell is an associate professor in the Department of Computer Science and Engineering at Texas A&M University. His research focuses on systems that exploit their physical embedding to interact with the world, working to understand, design, and build such systems. He has published papers on multi-robot task allocation, biologically inspired multiple-robot systems, estimation of group-level swarm properties, minimalist- and multi-robot manipulation, rigid-body simulation and contact models, human-robot interaction, and robotic theatre. The National Science Foundation, the Department of Energy, and DARPA have funded Shell’s work. He has been the recipient of an NSF Career award, the Montague Teaching award, the George Bekey Service award, and multiple best reviewer awards.