ECE Seminar: A Real-Time Algorithmic Framework for Robust and Risk-Sensitive Planning and Decision-Making
Mon, Apr 08, 2019 @ 11:00 AM - 12:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sumeet Singh, PhD Candidate, Dept of Aeronautics & Astronautics, Stanford University
Talk Title: A Real-Time Algorithmic Framework for Robust and Risk-Sensitive Planning and Decision-Making
Abstract: Integrating autonomous robots into safety-critical settings requires reasoning about uncertainty at all levels of the autonomy stack. In this talk, I will present novel algorithmic tools leveraging Lyapunov-based analysis, convex optimization, and risk measures to address robustness in robotic motion planning and decision-making under uncertainty. In the first part of the talk, by harnessing the theories of incremental stability and contraction, I will describe a unified framework for synthesizing robust trajectory tracking controllers for complex underactuated nonlinear systems with analytical bounded-input-bounded-output disturbance rejection guarantees. These results will be combined with computational tools drawn from semi-infinite convex programming to design real-time motion planning algorithms with certifiable safety guarantees. In addition, I will illustrate how to leverage these tools for sample-efficient model-based reinforcement learning with control-theoretic guarantees. In the second part of the talk, I will describe a framework for lifting notions of robustness from low-level motion planning to higher-level sequential decision-making using the theory of risk measures. Specifically, by leveraging a specific class of risk measures with favorable axiomatic foundations, I will demonstrate how to design decision-making algorithms with tuneable robustness properties. I will then discuss a novel application of this framework to inverse reinforcement learning for humans in safety-critical scenarios. The domains of aerial robotics and autonomous cars will be used throughout the talk as running examples.
Biography: Sumeet Singh is a Ph.D. candidate in the Autonomous Systems Lab in the Aeronautics and Astronautics Department at Stanford University. He received a B.Eng. in Mechanical Engineering and a Diploma of Music (Performance) from University of Melbourne in 2012, and a M.Sc. in Aeronautics and Astronautics from Stanford University in 2015. Prior to joining Stanford, Sumeet worked in the Berkeley Micromechanical Analysis and Design lab at the University of California, Berkeley in 2011 and the Aeromechanics Branch at NASA Ames in 2013. Sumeet's research interests include (1) Robust motion planning for constrained nonlinear systems, (2) Risk-sensitive inference and decision-making with humans in-the-loop, and (3) Design of verifiable learning architectures for safety-critical applications. Sumeet is the recipient of the Stanford Graduate Fellowship (2013-2016), the most prestigious Stanford fellowship awarded to incoming graduate students, and the Qualcomm Innovation Fellowship (2018).
Host: Professor Massoud Pedram, email@example.com
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher