Bayesian Relational Memory for Semantic Visual Navigation

We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards. BRM takes the form of a probabilistic relation graph over seman...

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Bibliographic Details
Published in2019 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 2769 - 2779
Main Authors Wu, Yi, Wu, Yuxin, Tamar, Aviv, Russell, Stuart, Gkioxari, Georgia, Tian, Yuandong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Bayes methods
Layout
Navigation
Planning
Probabilistic logic
Semantics
Visualization
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Summary:We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards. BRM takes the form of a probabilistic relation graph over semantic entities (e.g., room types), which allows (1) capturing the layout prior from training environments, i.e., prior knowledge, (2) estimating posterior layout at test time, i.e., memory update, and (3) efficient planning for navigation, altogether. We develop a BRM agent consisting of a BRM module for producing sub-goals and a goal-conditioned locomotion module for control. When testing in unseen environments, the BRM agent outperforms baselines that do not explicitly utilize the probabilistic relational memory structure.
ISSN:2380-7504
DOI:10.1109/ICCV.2019.00286