Learning Continuous Cost-to-Go Functions for Non-holonomic Systems

Jinwook Huh, Daniel D. Lee, Volkan Isler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems directly from the workspace description. Supervision from informative examples reduces training time and improves network performance. The manifold representing the optimal trajectories of a non-holonomic system has high-curvature regions which can not be efficiently captured with uniform sampling. To address this challenge, we present an adaptive sampling method which makes use of sampling based planners along with local, closed-form solutions to generate training samples. The cost-to-go function over a specific workspace is represented as a neural network whose weights are generated by a second, higher order network. The networks are trained in an end-to-end fashion. In our previous work, this architecture was shown to successfully learn to generate the cost-to-go functions of holonomic systems using uniform sampling. In this work, we show that uniform sampling fails for non-holonomic systems. However, with the proposed adaptive sampling methodology, our network can generate near-optimal trajectories for non-holonomic systems while avoiding obstacles. Experiments show that our method is two orders of magnitude faster compared to traditional approaches in cluttered environments.

Original languageEnglish (US)
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5772-5779
Number of pages8
ISBN (Electronic)9781665417143
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 - Prague, Czech Republic
Duration: Sep 27 2021Oct 1 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Country/TerritoryCzech Republic
CityPrague
Period9/27/2110/1/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Fingerprint

Dive into the research topics of 'Learning Continuous Cost-to-Go Functions for Non-holonomic Systems'. Together they form a unique fingerprint.

Cite this