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Visual Navigation Using Topological Maps
A Behavioral Approach to Visual Navigation with Graph Localization Networks
Motivated by research in psychology, we introduce a behavioral approach for visual navigation using topological maps.
Our goal is to enable a robot to navigate from one location to another, relying only on its visual input and the topological map of the environment.
We propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive primitivebehaviors implemented as convolutional or recurrent neural networks.
Using the gibson simulator , we verify that our approach outperforms relevant baselinesand is able to navigate in both seen and unseen environments.
Kevin Chen, Juan Pablo de Vicente, Gabriel Sepulveda, Fei Xia, Alvaro Soto, Marynel Vazquez, Silvio Savarese
Graph neural networks
Convolutional or recurrent neural networks
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