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@InProceedings{CoRL20-Park,
  author = {Jin-Soo Park and Brian Tsang and Harel Yedidsion and Garrett Warnell and Daehyun Kyoung and Peter Stone},
  title = {Learning to Improve Multi-Robot Hallway Navigation},
  booktitle = {Proceedings of the 4th Conference on Robot Learning (CoRL)},
  location = {Virtual Conference},
  month = {November},
  year = {2020},
  wwwnote={<a href="https://youtu.be/i4QGXfNSJSo">Video presentation</a>},
  abstract = {
As multi-robot applications become more prevalent, it becomes necessary to deve
lop navigation systems which allow autonomous mobile robots to efficiently and 
safely pass each other in confined spaces. Existing navigation systems, such as 
the widely used ROS Navigation Stack, usually produce safe, collision free path
s in static environments. However, these systems are not perfect, and when mult
iple mobile robots simultaneously navigate in narrow spaces, collisions and tur
narounds are not uncommon. Fine-tuning and enhancing such navigation stacks is 
not as simple as it looks since they are made up of multiple layers of code, an
d there exists a tradeoff between optimizing for efficiency, i.e. minimizing ti
me to destination (TTD) vs. optimizing for safety, i.e. minimizing collisions, 
with each objective leading to a different combination of parameter values. In 
this paper we develop a methodology to improve existing navigation stacks with 
regards to both objectives, without tuning their parameters, while preserving t
heir inherent safety control properties. Our proposed approach is a decentraliz
ed learning-based approach that is geared toward real world robotic deployment,
 by requiring little computing resources. It is agnostic of the underlying navi
gation stack and can adapt to different types of environmental layouts (i.e., h
allway structures).
  },
}