Vision-based navigation using deep reinforcement learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00334856" target="_blank" >RIV/68407700:21230/19:00334856 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/19:00334856
Result on the web
<a href="https://doi.org/10.1109/ECMR.2019.8870964" target="_blank" >https://doi.org/10.1109/ECMR.2019.8870964</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ECMR.2019.8870964" target="_blank" >10.1109/ECMR.2019.8870964</a>

Result language
angličtina
Original language name
Vision-based navigation using deep reinforcement learning
Original language description
Deep reinforcement learning (RL) has been successfully applied to a variety of game-like environments. However, the application of deep RL to visual navigation with realistic environments is a challenging task. We propose a novel learning architecture capable of navigating an agent, e.g. a mobile robot, to a target given by an image. To achieve this, we have extended the batched A2C algorithm with auxiliary tasks designed to improve visual navigation performance. We propose three additional auxiliary tasks: predicting the segmentation of the observation image and of the target image and predicting the depth-map. These tasks enable the use of supervised learning to pre-train a major part of the network and to reduce the number of training steps substantially. The training performance has been further improved by increasing the environment complexity gradually over time. An efficient neural network structure is proposed, which is capable of learning for multiple targets in multiple environments. Our method navigates in continuous state spaces and on the AI2-THOR environment simulator surpasses the performance of state-of-the-art goal-oriented visual navigation methods from the literature.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of European Conference on Mobile Robots
ISBN
978-1-7281-3605-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
Czech Technical University
Place of publication
Prague
Event location
Prague
Event date
Aug 4, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000558081900059

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