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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 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.
@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>},
}
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