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Occupancy Anticipation for Efficient Exploration and Navigation

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12350)

Abstract

State-of-the-art navigation methods leverage a spatial memory to generalize to new environments, but their occupancy maps are limited to capturing the geometric structures directly observed by the agent. We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. In doing so, the agent builds its spatial awareness more rapidly, which facilitates efficient exploration and navigation in 3D environments. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment, with performance significantly better than strong baselines. Furthermore, when deployed for the sequential decision-making tasks of exploration and navigation, our model outperforms state-of-the-art methods on the Gibson and Matterport3D datasets. Our approach is the winning entry in the 2020 Habitat PointNav Challenge. Project page: http://vision.cs.utexas.edu/projects/occupancy_anticipation/.

Notes

Acknowledgements

UT Austin is supported in part by DARPA Lifelong Learning Machines and the GCP Research Credits Program. We thank Devendra Singh Chaplot for clarifying the implementation details for ANS.

Supplementary material

Supplementary material 1 (mp4 11976 KB)

Supplementary material 2 (mp4 11378 KB)

Supplementary material 3 (mp4 12684 KB)

Supplementary material 4 (mp4 11451 KB)

Supplementary material 5 (mp4 50334 KB)

504441_1_En_24_MOESM6_ESM.pdf (4 mb)
Supplementary material 6 (pdf 4079 KB)

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Authors and Affiliations

  1. 1.The University of Texas at AustinAustinUSA
  2. 2.Facebook AI ResearchAustinUSA

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