NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments
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Author / Producer
Date
2021
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Robot navigation is a task where reinforcement learning approaches are still unable to compete with traditional path planning. State-of-the-art methods differ in small ways, and do not all provide reproducible, openly available implementations. This makes comparing methods a challenge. Recent research has shown that unsupervised learning methods can scale impressively, and be leveraged to solve difficult problems. In this work, we design ways in which unsupervised learning can be used to assist reinforcement learning for robot navigation. We train two end-to-end, and 18 unsupervised-learning-based architectures, and compare them, along with existing approaches, in unseen test cases. We demonstrate our approach working on a real life robot. Our results show that unsupervised learning methods are competitive with end-to-end methods. We also highlight the importance of various components such as input representation, predictive unsupervised learning, and latent features. We make all our models publicly available, as well as training and testing environments, and tools 1 . This release also includes OpenAI-gym-compatible environments designed to emulate the training conditions described by other papers, with as much fidelity as possible. Our hope is that this helps in bringing together the field of RL for robot navigation, and allows meaningful comparisons across state-of-the-art methods.
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Publication status
published
Editor
Book title
2021 IEEE International Conference on Robotics and Automation (ICRA)
Journal / series
Volume
Pages / Article No.
7829 - 7835
Publisher
IEEE
Event
IEEE International Conference on Robotics and Automation (ICRA 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03737 - Siegwart, Roland Y. (emeritus) / Siegwart, Roland Y. (emeritus)
Notes
Conference lecture held on June 1, 2021
Funding
779942 - CROWDBOT (EC)