Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning
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Date
2022-10
Publication Type
Journal Article
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yes
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Abstract
This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks fail to generalize well to unseen environments, the agent collects images of the new environment which are then used for self-supervised domain adaptation. We formulate this as an informative path planning problem, and present a novel information gain that leverages uncertainty extracted from the semantic model to safely collect relevant data. As domain adaptation progresses, these uncertainties change over time and the rapid learning feedback of our system drives the agent to collect different data. Experiments show that our method adapts to new environments faster and with higher final performance compared to an exploration objective, and can successfully be deployed to real-world environments on physical robots.
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published
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Journal / series
Volume
7 (4)
Pages / Article No.
8691 - 8698
Publisher
IEEE
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Software
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Subject
perception-action coupling; integrated planning and learning; object detection; segmentation and categorization