"Trustworthy Autonomy: Algorithms for Human-Robot Systems"

Katherine Driggs-Campbell, Postdoctoral Research Scholar, Stanford University

February 28th (Wednesday), 11:00am
Harold Frank Hall (HFH), Rm 4164 (ECE Conf. Rm.)

Autonomous systems, such as self-driving cars, are becoming tangible technologies that will soon impact the human experience. However, the desirable impacts of autonomy are only achievable if the underlying algorithms can handle the unique challenges humans present: People tend to defy expected behaviors and do not conform to many of the standard assumptions made in robotics. To design safe, trustworthy autonomy, we must transform how intelligent systems interact, influence, and predict human agents. In this work, we’ll use tools from robotics, artificial intelligence, and control to explore and uncover structure in complex human-robot systems to create more intelligent, interactive autonomy.

In this talk, I’ll present on robust prediction methods that allow us to predict driving behavior over long time horizons with very high accuracy. These methods have been applied to intervention schemes for semi-autonomous vehicles and to autonomous planning that considers nuanced interactions during cooperative maneuvers. I’ll also present a new framework for multi-agent perception that uses people as sensors to improve mapping. By observing the actions of human agents, we demonstrate how we can make inferences about occluded regions and, in turn, improve control. Finally, I’ll present on recent efforts on validating stochastic systems, merging deep learning and control, and implementing these algorithms on a fully equipped test vehicle that can operate safely on the road.

About Katherine Driggs-Campbell:

Katie is currently a Postdoctoral Research Scholar at the Stanford Intelligent Systems Laboratory in the Aeronautics and Astronautics Department. She received a B.S.E. with honors from Arizona State University in 2012 and an M.S. from UC Berkeley in 2015. In May of 2017, she earned her PhD in Electrical Engineering and Computer Sciences from the University of California, Berkeley, advised by Professor Ruzena Bajcsy. Her thesis was entitled “Tools for Trustworthy Autonomy: Robust Prediction, Intuitive Control, and Optimized Interaction,” which contributed to the field of autonomy, by merging ideas robotics, transportation, and control to address problems associated with human-in-the-loop. Her work considers the integration of autonomy into human dominated fields, in terms of safe interaction, with a strong emphasis on novel modeling methods, experimental design, robust learning, and control frameworks. She received the Demetri Angelakos Memorial Achievement Award for her contributions to the community, has instigated many events and groups for women in STEM, including founding a group for Women in Intelligent Transportation Systems, and was selected for the Rising Stars in EECS program in 2017.

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