• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •
A Lifelong Learning Approach to Mobile Robot Navigation.
Bo Liu, Xuesu Xiao, and Peter Stone.
IEEE
Robotics and Automation Letters (RA-L), 6(2), April 2021.
Presented at IEEE International Conference on Robotics
and Automation (ICRA),
Video presentation
This paper presents a self-improving lifelong learning framework for a mobilerobot navigating in differ- ent environments. Classical static navigationmethods require environment-specific in-situ system adjustment, e.g. fromhuman experts, or may repeat their mistakes regardless of how many times theyhave navigated in the same environment. Having the potential to improve withexperience, learning- based navigation is highly dependent on access to trainingresources, e.g. sufficient memory and fast computation, and is prone toforgetting previously learned capability, especially when facing differentenvironments. In this work, we propose Lifelong Learning for Navigation (LLfN)which (1) improves a mobile robot’s navigation behavior purely based on its ownexperience, and (2) retains the robot’s capability to navigate in previousenvironments after learning in new ones. LLfN is implemented and testedentirely onboard a physical robot with a limited memory and computation budget.
@article{ICRA2021-Liu,
author = {Bo Liu and Xuesu Xiao and Peter Stone},
title = {A Lifelong Learning Approach to Mobile Robot Navigation},
journal={IEEE Robotics and Automation Letters (RA-L)},
volume="6",
number="2",
month = {April},
year = {2021},
abstract = {
This paper presents a self-improving lifelong learning framework for a mobile
robot navigating in differ- ent environments. Classical static navigation
methods require environment-specific in-situ system adjustment, e.g. from
human experts, or may repeat their mistakes regardless of how many times they
have navigated in the same environment. Having the potential to improve with
experience, learning- based navigation is highly dependent on access to training
resources, e.g. sufficient memory and fast computation, and is prone to
forgetting previously learned capability, especially when facing different
environments. In this work, we propose Lifelong Learning for Navigation (LLfN)
which (1) improves a mobile robot’s navigation behavior purely based on its own
experience, and (2) retains the robot’s capability to navigate in previous
environments after learning in new ones. LLfN is implemented and tested
entirely onboard a physical robot with a limited memory and computation budget.
},
wwwnote={Presented at IEEE International Conference on Robotics and Automation (ICRA),<br>
<a href="www.youtube.com/watch?v=ja_Rjc63xiY&t=68s.">Video presentation</a>},
}
Generated by bib2html.pl (written by Patrick Riley ) on Wed Apr 17, 2024 18:42:46