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APPLR: Adaptive Planner Parameter Learning from Reinforcement (2021)
Zifan Xu and Gauraang Dhamankar and Anirudh Nair and Xuesu Xiao and
Garrett Warnell
and
Bo Liu
and Zizhao Wang and
Peter Stone
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.
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PDF
Citation:
In
Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA 2021)
, Xi'an, China, June 2021.
Bibtex:
@inproceedings{icra21-xu, title={APPLR: Adaptive Planner Parameter Learning from Reinforcement}, author={Zifan Xu and Gauraang Dhamankar and Anirudh Nair and Xuesu Xiao and Garrett Warnell and Bo Liu and Zizhao Wang and Peter Stone}, booktitle={Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA 2021)}, month={June}, address={Xi'an, China}, url="http://nn.cs.utexas.edu/?icra21-xu", year={2021} }
People
Bo Liu
Peter Stone
pstone [at] cs utexas edu
Garrett Warnell
warnellg [at] cs utexas edu
Areas of Interest
Autonomous Driving
Robotics