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APPLI: Adaptive Planner Parameter Learning From Interventions.
Zizhao Wang,
Xuesu Xiao, Bo Liu, Garrett
Warnell, and Peter Stone.
In Proceedings of the International Conference
on Robotics and Automation (ICRA 2021), May 2021.
Video presentation
While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptimal behavior in certain scenarios. The current practice in such scenarios is to manually re-tune the system' parameters, e.g. max speed, sampling rate, inflation radius, to optimize performance. This practice requires expert knowledge and may jeopardize performance in the originally good scenarios. Meanwhile, it is relatively easy for a human to identify those failure or suboptimal cases and provide a teleoperated intervention to correct the failure or suboptimal behavior. In this work, we seek to learn from those human interventions to improve navigation performance. In particular, we propose Adaptive Planner Parameter Learning from Interventions (APPLI), in which multiple sets of navigation parameters are learned during training and applied based on a confidence measure to the underlying navigation system during deployment. In our physical experiments, the robot achieves better performance compared to the planner with static default parameters, and even dynamic parameters learned from a full human demonstration. We also show APPLI's generalizability in another unseen physical test course, and a suite of 300 simulated navigation environments.
@InProceedings{icra21-wang,
author = {Zizhao Wang and Xuesu Xiao and Bo Liu and Garrett Warnell and Peter Stone},
title = {{APPLI}: Adaptive Planner Parameter Learning From Interventions},
booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA 2021)},
location = {Xi'an, China},
month = {May},
year = {2021},
abstract = {
While classical autonomous navigation systems can typically move robots from
one point to another safely and in a collision-free manner, these systems may
fail or produce suboptimal behavior in certain scenarios.
The current practice in such scenarios is to manually re-tune the system'
parameters, e.g. max speed, sampling rate, inflation radius, to optimize
performance. This practice requires expert knowledge and may jeopardize
performance in the originally good scenarios.
Meanwhile, it is relatively easy for a human to identify those failure or
suboptimal cases and provide a teleoperated intervention to correct the
failure or suboptimal behavior. In this work, we seek to learn from those
human interventions to improve navigation performance. In particular, we
propose Adaptive Planner Parameter Learning from Interventions (APPLI), in
which multiple sets of navigation parameters are learned during training and
applied based on a confidence measure to the underlying navigation system
during deployment. In our physical experiments, the robot achieves better
performance compared to the planner with static default parameters, and even
dynamic parameters learned from a full human demonstration. We also show
APPLI's generalizability in another unseen physical test course, and a suite
of 300 simulated navigation environments.
},
wwwnote={<a href="https://youtu.be/fOXkrS7Mwyk">Video presentation</a>},
}
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