Planning via model checking with decision-tree controllers

Jonis Kiesbye, Kush Grover, Pranav Ashok, Jan Kretinsky

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

1 Scopus citations

Abstract

Planning problems can be solved not only by planners, but also by model checkers. While the former yield a plan that requires replanning as soon as any fault occurs, the latter provide a 'universal' plan (a.k.a. strategy, policy, or controller) able to make decisions under all circumstances. One of the prohibitive aspects of the latter approach is stemming from this very advantage: since it is defined for all possible states of the system, it is typically so large that it does not fit into small memories of embedded devices. As another consequence of the size, its execution may be slow. In this paper, we provide a solution to this issue by linking the model checkers with decision-tree learners, resulting in decision-tree representations of the synthesized strategies. Not only are they dramatically smaller, but also more explainable and orders-of-magnitude faster to execute than plans with replanning. In addition, we describe a method for model validation and debugging via the model checker and the decision-tree learner in the loop. We illustrate the approach on our case study of a robotic arm for picking items in a real industrial setting.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4347-4354
Number of pages8
ISBN (Electronic)9781728196817
DOIs
StatePublished - 2022
Event39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States
Duration: 23 May 202227 May 2022

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference39th IEEE International Conference on Robotics and Automation, ICRA 2022
Country/TerritoryUnited States
CityPhiladelphia
Period23/05/2227/05/22

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