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Benchmarking Reinforcement Learning Techniques for Autonomous Navigation.
Zifan
Xu, Bo Liu, Xuesu Xiao, Anirudh
Nair, and Peter Stone.
In Proceedings of the 2023 IEEE International
Conference on Robotics and Automation (ICRA 2023), May 2023.
Deep reinforcement learning (RL) has broughtmany successes for autonomous robot navigation. However,there still exists important limitations that prevent real-worlduse of RL-based navigation systems. For example, most learningapproaches lack safety guarantees; and learned navigationsystems may not generalize well to unseen environments.Despite a variety of recent learning techniques to tackle thesechallenges in general, a lack of an open-source benchmarkand reproducible learning methods specifically for autonomousnavigation makes it difficult for roboticists to choose whatlearning methods to use for their mobile robots and for learningresearchers to identify current shortcomings of general learningmethods for autonomous navigation. In this paper, we identifyfour major desiderata of applying deep RL approaches forautonomous navigation: (D1) reasoning under uncertainty, (D2)safety, (D3) learning from limited trial-and-error data, and (D4)generalization to diverse and novel environments. Then, weexplore four major classes of learning techniques with thepurpose of achieving one or more of the four desiderata:memory-based neural network architectures (D1), safe RL (D2),model-based RL (D2, D3), and domain randomization (D4). Bydeploying these learning techniques in a new open-source large-scale navigation benchmark and real-world environments, weperform a comprehensive study aimed at establishing to whatextent can these techniques achieve these desiderata for RL-based navigation systems
@InProceedings{ICRA2023-ZIFAN,
author = {Zifan Xu and Bo Liu and Xuesu Xiao and Anirudh Nair and Peter Stone},
title = {Benchmarking Reinforcement Learning Techniques for Autonomous Navigation},
booktitle = {Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023)},
location = {London, England},
month = {May},
year = {2023},
abstract = {
Deep reinforcement learning (RL) has brought
many successes for autonomous robot navigation. However,
there still exists important limitations that prevent real-world
use of RL-based navigation systems. For example, most learning
approaches lack safety guarantees; and learned navigation
systems may not generalize well to unseen environments.
Despite a variety of recent learning techniques to tackle these
challenges in general, a lack of an open-source benchmark
and reproducible learning methods specifically for autonomous
navigation makes it difficult for roboticists to choose what
learning methods to use for their mobile robots and for learning
researchers to identify current shortcomings of general learning
methods for autonomous navigation. In this paper, we identify
four major desiderata of applying deep RL approaches for
autonomous navigation: (D1) reasoning under uncertainty, (D2)
safety, (D3) learning from limited trial-and-error data, and (D4)
generalization to diverse and novel environments. Then, we
explore four major classes of learning techniques with the
purpose of achieving one or more of the four desiderata:
memory-based neural network architectures (D1), safe RL (D2),
model-based RL (D2, D3), and domain randomization (D4). By
deploying these learning techniques in a new open-source large-
scale navigation benchmark and real-world environments, we
perform a comprehensive study aimed at establishing to what
extent can these techniques achieve these desiderata for RL-
based navigation systems
},
}
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