Verifying Learning-Based Robotic Navigation Systems

Guy Amir*, Davide Corsi, Raz Yerushalmi, Luca Marzari, David Harel, Alessandro Farinelli, Guy Katz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for tasks where complex policies are learned within reactive systems. Unfortunately, these policies are known to be susceptible to bugs. Despite significant progress in DNN verification, there has been little work demonstrating the use of modern verification tools on real-world, DRL-controlled systems. In this case study, we attempt to begin bridging this gap, and focus on the important task of mapless robotic navigation — a classic robotics problem, in which a robot, usually controlled by a DRL agent, needs to efficiently and safely navigate through an unknown arena towards a target. We demonstrate how modern verification engines can be used for effective model selection, i.e., selecting the best available policy for the robot in question from a pool of candidate policies. Specifically, we use verification to detect and rule out policies that may demonstrate suboptimal behavior, such as collisions and infinite loops. We also apply verification to identify models with overly conservative behavior, thus allowing users to choose superior policies, which might be better at finding shorter paths to a target. To validate our work, we conducted extensive experiments on an actual robot, and confirmed that the suboptimal policies detected by our method were indeed flawed. We also demonstrate the superiority of our verification-driven approach over state-of-the-art, gradient attacks. Our work is the first to establish the usefulness of DNN verification in identifying and filtering out suboptimal DRL policies in real-world robots, and we believe that the methods presented here are applicable to a wide range of systems that incorporate deep-learning-based agents.

Original languageAmerican English
Title of host publicationTools and Algorithms for the Construction and Analysis of Systems - 29th International Conference, TACAS 2023, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022, Proceedings
EditorsSriram Sankaranarayanan, Natasha Sharygina
PublisherSpringer Science and Business Media Deutschland GmbH
Pages607-627
Number of pages21
ISBN (Print)9783031308222
DOIs
StatePublished - 2023
Event29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2023, held as part of the 26th European Joint Conferences on Theory and Practice of Software, ETAPS 2023 - Paris, France
Duration: 22 Apr 202327 Apr 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13993 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2023, held as part of the 26th European Joint Conferences on Theory and Practice of Software, ETAPS 2023
Country/TerritoryFrance
CityParis
Period22/04/2327/04/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

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