Robust and Risk-Sensitive Planning via Contraction Theory and Convex Optimization

Speaker: Sumeet Singh , Stanford University

Date: Tuesday, February 14, 2017

Time: 11:00 AM to 12:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: 32-G449 (Patil/Kiva)

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Host: Russ Tedrake

Contact: Stephen Proulx, steve@csail.mit.edu

Relevant URL: http://asl.stanford.edu/people/sumeet-singh/

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Reminders to: robotics@mit.edu, seminars@csail.mit.edu

Reminder Subject: TALK: Robust and Risk-Sensitive Planning via Contraction Theory and Convex Optimization

Abstract:

A key prerequisite for autonomous robots working alongside humans is the ability to cope with uncertainty at two levels: (1) low-level modeling errors or external disturbances, and (2) high-level uncertainty about the humansÂ’ goals and actions. For the first part of this talk, I will present our framework for the online generation of robust motion plans for constrained nonlinear robotic systems such as UAVs subject to bounded disturbances while operating in cluttered environments. Specifically, by leveraging tools from contraction theory and convex optimization, we are able to provide a guaranteed margin of safety (i.e., a precise buffer zone) for any desired trajectory, thereby guaranteeing the safe, collision-free execution of the resulting motion plan. Having addressed this robust low-level control strategy, in the second part of the talk I will discuss our recent work on risk- and ambiguity- sensitive Inverse Reinforcement Learning for better capturing human decision making. In particular, by departing from the ubiquitous expected utility framework and proposing a flexible model using coherent risk metrics, we are able to capture an entire spectrum of risk preferences from risk-neutral to worst-case. This allows us to better predict the human decision making process, both qualitatively and quantitatively. We envision that leveraging such a methodology is an important step toward more reliable high- and low- level control processes for safety-critical robotics systems operating in shared environments.

Bio:

Sumeet Singh is a Ph.D. candidate in Aeronautics and Astronautics 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 University of California Berkeley in 2011 and the Aeromechanics Branch at NASA Ames in 2013. SumeetÂ’s current research interests are twofold: 1) Robust motion planning for constrained nonlinear systems, and 2) Risk-sensitive Model Predictive Control (MPC). Within the first topic, Sumeet is investigating the design of nonlinear control algorithms for online generation of robust motion plans with guaranteed margins of safety for constrained robotic systems in cluttered environments. The second topic focuses on the development and analysis of stochastic MPC algorithms for robust and risk-sensitive decision making problems.

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Created by Stephen Proulx Email at Friday, February 03, 2017 at 10:23 AM.