Towards Generalization in Target-Driven Visual Navigation by Using Deep Reinforcement Learning

Among the main challenges in robotics, target-driven visual navigation has gained increasing interest in recent years. In this task, an agent has to navigate in an environment to reach a user specified target, only through vision. Recent fruitful approaches rely on deep reinforcement learning, which...

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Vydané v:IEEE transactions on robotics Ročník 36; číslo 5; s. 1546 - 1561
Hlavní autori: Devo, Alessandro, Mezzetti, Giacomo, Costante, Gabriele, Fravolini, Mario L., Valigi, Paolo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Computer simulation
Deep learning
Deep learning in robotics and automation
Machine learning
Navigation
Robotics
Simultaneous localization and mapping
target-driven visual navigation
Task analysis
Training
visual learning
visual-based navigation
Visualization
ISSN:1552-3098, 1941-0468
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Shrnutí:Among the main challenges in robotics, target-driven visual navigation has gained increasing interest in recent years. In this task, an agent has to navigate in an environment to reach a user specified target, only through vision. Recent fruitful approaches rely on deep reinforcement learning, which has proven to be an effective framework to learn navigation policies. However, current state-of-the-art methods require to retrain, or at least fine-tune, the model for every new environment and object. In real scenarios, this operation can be extremely challenging or even dangerous. For these reasons, we address generalization in target-driven visual navigation by proposing a novel architecture composed of two networks, both exclusively trained in simulation. The first one has the objective of exploring the environment, while the other one of locating the target. They are specifically designed to work together, while separately trained to help generalization. In this article, we test our agent in both simulated and real scenarios, and validate its generalization capabilities through extensive experiments with previously unseen goals and unknown mazes, even much larger than the ones used for training.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2020.2994002