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Embodied Contrastive Learning with Geometric Consistency and Behavioral Awareness for Object Navigation

Published: 28 October 2024 Publication History

Abstract

Object Navigation (ObjcetNav), which enables an agent to seek any instance of an object category specified by a semantic label, has shown great advances. However, current agents are built upon occlusion-prone visual observations or compressed 2D semantic maps, which hinder their embodied perception of 3D scene geometry and easily lead to ambiguous object localization and blind exploration. To address these limitations, we present an Embodied Contrastive Learning (ECL) method with Geometric Consistency (GC) and Behavioral Awareness (BA), which motivates agents to actively encode 3D scene layouts and semantic cues. Driven by our embodied exploration strategy, BA is modeled by predicting navigational actions based on multi-frame visual images, as behaviors that cause differences between adjacent visual sensations are crucial for learning correlations among continuous visions. The GC is modeled as the alignment of behavior-aware visual stimulus with 3D semantic shapes by employing unsupervised contrastive learning. The aligned behavior-aware visual features and geometric invariance priors are injected into a modular ObjectNav framework to enhance object recognition and exploration capabilities. As expected, our ECL method performs well on object detection and instance segmentation tasks. Our ObjectNav strategy outperforms state-of-the-art methods on MP3D and Gibson datasets, showing the potential of our ECL in embodied navigation.

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      cover image ACM Conferences
      MM '24: Proceedings of the 32nd ACM International Conference on Multimedia
      October 2024
      11719 pages
      ISBN:9798400706868
      DOI:10.1145/3664647
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      Published: 28 October 2024

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      Author Tags

      1. behavioral awareness
      2. contrastive representation learning
      3. embodied ai
      4. geometric consistency
      5. object navigation

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      MM '24: The 32nd ACM International Conference on Multimedia
      October 28 - November 1, 2024
      Melbourne VIC, Australia

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      MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
      Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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