Studies in Computational Intelligence, volume 925 SCI, pages 212-221
Learning Embodied Agents with Policy Gradients to Navigate in Realistic Environments
Staroverov Alexey
1
,
Vetlin Vladislav
2
,
Makarenko Stepan
1
,
Naumov Anton
2
,
Panov Aleksandr I
1, 3
Publication type: Book Chapter
Publication date: 2020-10-02
Quartile SCImago
Q4
Quartile WOS
—
Impact factor: —
ISSN: 1860949X, 18609503
Abstract
Indoor navigation is one of the main tasks in robotic systems. Most decisions in this area rely on ideal agent coordinates and a pre-known room map. However, the high accuracy of indoor localization cannot be achieved in realistic scenarios. For example, the GPS has low accuracy in the room; odometry often gives much noise for accurate positioning, etc. In this paper, we conducted a study of the navigation problem in the realistic Habitat simulator. We proposed a method based on the neural network approach and reinforcement learning that takes into account these factors. The most promising recent approaches were DDPPO and ANM for agent control and DF-VO for localization, during the analysis of which a new approach was developed. This method takes into account the non-determinism of the robot’s actions and the noise level of data from its sensors.
Citations by journals
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Lecture Notes in Networks and Systems
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Lecture Notes in Networks and Systems
2 publications, 100%
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1
2
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Citations by publishers
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1
2
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Springer Nature
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Springer Nature
2 publications, 100%
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1
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Staroverov A. et al. Learning Embodied Agents with Policy Gradients to Navigate in Realistic Environments // Studies in Computational Intelligence. 2020. Vol. 925 SCI. pp. 212-221.
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Staroverov A., Vetlin V., Makarenko S., Naumov A., Panov A. I. Learning Embodied Agents with Policy Gradients to Navigate in Realistic Environments // Studies in Computational Intelligence. 2020. Vol. 925 SCI. pp. 212-221.
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TY - GENERIC
DO - 10.1007/978-3-030-60577-3_24
UR - https://doi.org/10.1007%2F978-3-030-60577-3_24
TI - Learning Embodied Agents with Policy Gradients to Navigate in Realistic Environments
T2 - Studies in Computational Intelligence
AU - Staroverov, Alexey
AU - Vetlin, Vladislav
AU - Makarenko, Stepan
AU - Naumov, Anton
AU - Panov, Aleksandr I
PY - 2020
DA - 2020/10/02 00:00:00
PB - Springer Nature
SP - 212-221
VL - 925 SCI
SN - 1860-949X
SN - 1860-9503
ER -
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@incollection{2020_Staroverov,
author = {Alexey Staroverov and Vladislav Vetlin and Stepan Makarenko and Anton Naumov and Aleksandr I Panov},
title = {Learning Embodied Agents with Policy Gradients to Navigate in Realistic Environments},
publisher = {Springer Nature},
year = {2020},
volume = {925 SCI},
pages = {212--221},
month = {oct}
}
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