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Treasure Hunting: Embodied Contrastive Learning-Enhanced Coarse-to-Fine Object Seeking With Explorer and
IEEE Transactions on Neural Networks and Learning Systems
|November 20, 2025
Summary
This study introduces embodied contrastive learning (ECL) for 3D object navigation, improving 3D scene understanding and object localization. The novel approach enhances agent performance in complex environments.
Area of Science:
- Robotics and Artificial Intelligence
- Computer Vision
- Embodied AI
Background:
- Current object navigation (ObjectNav) agents rely on limited 2D maps or occlusion-prone visual data, hindering 3D scene geometry perception.
- Existing ObjectNav methods often separate exploration and exploitation, leading to poor object localization and inefficient exploration strategies.
- Embodied perception in 3D environments remains a challenge for autonomous agents.
Purpose of the Study:
- To develop an embodied contrastive learning (ECL) method that improves 3D scene layout and semantic cue encoding for object navigation.
- To propose a coarse-to-fine ObjectNav policy that enhances exploration and exploitation through cooperative agents.
- To advance the capabilities of embodied agents in complex, unseen 3D environments.
Main Methods:
- Embodied contrastive learning (ECL) incorporating geometric consistency (GC) and behavioral awareness (BA) for 3D scene understanding.
- Behavioral awareness (BA) modeled by predicting navigational actions from multi-frame visual inputs.
- Geometric consistency (GC) achieved by aligning visual stimuli with 3D semantic shapes via unsupervised contrastive learning.
- A coarse-to-fine ObjectNav policy utilizing an explorer-discriminator cooperation framework inspired by human treasure hunting.
Main Results:
- The proposed ECL method demonstrates strong performance on object detection (ObjDet) and instance segmentation (InstSeg) tasks.
- The ECL-enhanced ObjectNav strategy significantly outperforms state-of-the-art (SOTA) methods on benchmark datasets like Matterport3D (MP3D), Gibson, and HM3D.
- The cooperative explorer and discriminator agents effectively improve object localization and navigation efficiency.
Conclusions:
- The developed embodied contrastive learning method enhances agents' ability to perceive and encode 3D scene geometry and semantics.
- The novel coarse-to-fine ObjectNav policy with cooperative agents represents a significant advancement in embodied AI for navigation tasks.
- This research provides a robust framework for improving embodied agent performance in complex real-world scenarios.
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