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Title: Of Priors and Particles: Structured and Distributed Approaches to Robot Perception and Control
Date: Friday, May 28th, 2021
Time: 1:00 PM - 3:00 PM (EST)
Location: Zoom meeting (link: https://washington.zoom.us/j/97030172625?pwd=M2pkcnJLQkMwK3gyODdhU0Z2czN1UT09 )
Alexander (Sasha) Lambert
Robotics Ph.D Candidate
School of Electrical and Computer Engineering
Georgia Institute of Technology
Committee:
Dr. Byron Boots (Advisor) - School of Computer Science and Engineering, University of Washington
Dr. Sonia Chernova - School of Interactive Computing, Georgia Institute of Technology
Dr. Seth Hutchinson - School of Interactive Computing, Georgia Institute of Technology
Dr. Matthew Gombolay - School of Interactive Computing, Georgia Institute of Technology
Dr. Fabio Ramos - School of Computer Science, University of Sydney
Abstract:
Applications of robotic systems have expanded significantly in their scope, moving beyond the caged predictability of industrial automation and towards more open, unstructured environments. These agents must learn to reliably perceive their surroundings, efficiently integrate new information and quickly adapt to dynamic perturbations. To accomplish this, we require solutions which can effectively incorporate prior knowledge while maintaining the generality of learned representations. These systems must also contend with uncertainty in both their perception of the world and in predicting possible future outcomes. Efficient methods for probabilistic inference are then key to realizing robust, adaptive behavior.
This thesis will first examine data-driven approaches for learning and combining perceptual models for both visual and tactile sensor modalities, common in robotics. Modern variational inference methods will then be examined in the context of online optimization and stochastic optimal control. Specifically, the thesis will contribute (1) data-driven visual and tactile perceptual models leveraging kinematic and dynamic priors, (2) a framework for joint inference with visuo-tactile sensing, (3) a family of particle-based, variational model predictive control and planning algorithms, and (4) a distributed inference scheme for online model adaptation.
