• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •
APPLR: Adaptive Planner Parameter Learning from Reinforcement.
Zifan Xu,
Gauraang Dhamankar, Anirudh Nair, Xuesu Xiao, Garrett
Warnell, Bo Liu, Zizhao Wang,
and Peter Stone.
In Proceedings of the 2021 IEEE International Conference
on Robotics and Automation (ICRA 2021), June 2021.
Video
presentation
Project webpage
[PDF]3.4MB [slides.pptx]27.4MB
Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-tuning in order to work in new environments. To mitigate this requirement, it has been proposed to learn parameters for different contexts in a new environment using human demonstrations collected via teleoperation. However, learning from human demonstration limits deployment to the training environment, and limits overall performance to that of a potentially-suboptimal demonstrator. In this paper, we introduce APPLR, Adaptive Planner Parameter Learning from Reinforcement, which allows existing navigation systems to adapt to new scenarios by using a parameter selection scheme discovered via reinforcement learning (RL) in a wide variety of simulation environments. We evaluate APPLR on a robot in both simulated and physical experiments, and show that it can outperform both a fixed set of hand-tuned parameters and also a dynamic parameter tuning scheme learned from human demonstration.
@InProceedings{icra21-xu,
author = {Zifan Xu and Gauraang Dhamankar and Anirudh Nair and Xuesu Xiao and Garrett Warnell and Bo Liu and Zizhao Wang and Peter Stone},
title = {{APPLR}: Adaptive Planner Parameter Learning from Reinforcement},
booktitle = {Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA 2021)},
location = {Xi'an, China},
month = {June},
year = {2021},
abstract = {Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-tuning in order to work in new environments. To mitigate this requirement, it has been proposed to learn parameters for different contexts in a new environment using human demonstrations collected via teleoperation. However, learning from human demonstration limits deployment to the training environment, and limits overall performance to that of a potentially-suboptimal demonstrator. In this paper, we introduce APPLR, Adaptive Planner Parameter Learning from Reinforcement, which allows existing navigation systems to adapt to new scenarios by using a parameter selection scheme discovered via reinforcement learning (RL) in a wide variety of simulation environments. We evaluate APPLR on a robot in both simulated and physical experiments, and show that it can outperform both a fixed set of hand-tuned parameters and also a dynamic parameter tuning scheme learned from human demonstration.},
wwwnote={<a href="https://www.youtube.com/watch?v=JKHTAowdGUk&t=26s">Video presentation</a><br><a href="https://www.cs.utexas.edu/~xiao/Research/APPL/APPL.html">Project webpage</a>}
}
Generated by bib2html.pl (written by Patrick Riley ) on Sun Nov 24, 2024 20:24:54