Assistant Professor, University of Colorado Boulder
Friday, December 7, 2018, 3 PM
Woodward Hall, Room 147
Talk Abstract: The age of autonomous machines has arrived. Yet, as products of imperfect human engineering designed to make decisions in an uncertain world, the promise of “set-it-and-forget-it” autonomy is still quite far off: autonomous systems will never operate out of the box “exactly right”. For sufficiently rich tasks that constantly push the technological cutting edge, they will encounter unexpected situations that require reasoning beyond their designed/immediate capabilities. As such, an intelligent autonomous system must be able to independently gather, process, and act on imperfect information—and be cognizant of what it can and cannot accomplish, and know when and how to seek help.
Human-machine interaction is thus a key component of autonomous system design, and must naturally connect to existing perception, planning, learning, and reasoning algorithms that enable autonomy. An autonomous robot, for instance, should ideally enable human stakeholders and user to fluidly delegate tasks, assess available information, and contribute meaningful operation improvements—without having to “babysit”, act as “band aids” (when all else fails), or think too hard about what the system is trying to do. As teammates, intelligent autonomous robots should also be able to communicate with human users to leverage their complementary abilities to improve decision making under uncertainty.
This talk will present novel Bayesian approaches to collaborative human-robot reasoning under uncertainty that can be exploited from the outset in autonomous system design. The talk will focus on probabilistic modeling, inference, and optimization techniques for augmenting autonomous optimal state estimation and planning algorithms with “plug-and-play human sensors”, connected via user-friendly semantic natural language chat and free-form map sketching interfaces. Results from collaborative human-robot teaming applications for target search and tracking applications show that these techniques allow human-machine teams to gracefully “cut knots and fill in gaps” for challenging problems—without undermining individual agent roles or ignoring their limitations.