Fall Research Expo 2021

Verifiably Robust Sonar Perception

Analyzing high-dimensional data like camera images is an essential task in modern autonomous systems such as self-driving cars. However, this task, known as perception, is notoriously difficult to perform error-free when it is implemented with neural networks. In fact, there is typically no mathematical definition of what constitutes a correct output or an error. So one cannot precisely check the correctness of such perception or guide its training with precise definitions. This project seeks to overcome the above challenges by rethinking how perception is built, using formal analysis. Specifically, we aim to develop perception in a modeling and training loop, where we (1) create a mathematical model of the perception's inputs and outputs, (2) train a neural network based on this model, and (3) repeat if we cannot automatically prove correctness using a neural network analysis tool.

PRESENTED BY
PURM - Penn Undergraduate Research Mentoring Program
College of Arts & Sciences
CO-PRESENTERS
Advised By
Ivan Ruchkin
Postdoctoral Fellow
Radoslav Ivanov
Postdoctoral Fellow
Insup Lee
Professor in CIS
Oleg Sokolsky
Professor in CIS
Join Jack for a virtual discussion
PRESENTED BY
PURM - Penn Undergraduate Research Mentoring Program
College of Arts & Sciences
CO-PRESENTERS
Advised By
Ivan Ruchkin
Postdoctoral Fellow
Radoslav Ivanov
Postdoctoral Fellow
Insup Lee
Professor in CIS
Oleg Sokolsky
Professor in CIS

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