Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation

• URL: http://arxiv.org/abs/2007.09841v1
• Date: Mon, 20 Jul 2020 02:19:26 GMT
• Title: Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation
• Authors: Medhini Narasimhan, Erik Wijmans, Xinlei Chen, Trevor Darrell, Dhruv Batra, Devi Parikh, Amanpreet Singh
• Abstract summary: We introduce a learning-based approach for room navigation using semantic maps. We train a model to generate amodal semantic top-down maps indicating beliefs of location, size, and shape of rooms. Next, we use these maps to predict a point that lies in the target room and train a policy to navigate to the point.
• Score: 143.6144560164782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a learning-based approach for room navigation using semantic maps. Our proposed architecture learns to predict top-down belief maps of regions that lie beyond the agent's field of view while modeling architectural and stylistic regularities in houses. First, we train a model to generate amodal semantic top-down maps indicating beliefs of location, size, and shape of rooms by learning the underlying architectural patterns in houses. Next, we use these maps to predict a point that lies in the target room and train a policy to navigate to the point. We empirically demonstrate that by predicting semantic maps, the model learns common correlations found in houses and generalizes to novel environments. We also demonstrate that reducing the task of room navigation to point navigation improves the performance further.

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