2023/07/23 by Yicong Hong, Yang Zhou, Hong, Yicong +11 · 11 citations
Computer Science · Psychology · #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Cognition #Cognitive map #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Domain Adaptation and Few-Shot Learning #Encoder #FOS: Computer and information sciences #Human–computer interaction #Multimodal Machine Learning Applications #Psychology #Robot #Robotics (cs.RO) #Semantics (computer science)
paper · pdf · doi:10.48550/arxiv.2307.12335
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/07/23 · openalex created_date 2023/07/26 · openalex updated_date 2026/07/28
Being able to perceive the semantics and the spatial structure of the environment is essential for visual navigation of a household robot. However, most existing works only employ visual backbones pre-trained either with independent images for classification or with self-supervised learning methods to adapt to the indoor navigation domain, neglecting the spatial relationships that are essential to the learning of navigation. Inspired by the behavior that humans naturally build semantically and spatially meaningful cognitive maps in their brains during navigation, in this paper, we propose a novel navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps (Ego2-Map). We apply the visual transformer as the backbone encoder and train the model with data collected from the large-scale Habitat-Matterport3D environments. Ego2-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation. Experiments show that agents using our learned representations on object-goal navigation outperform recent visual pre-training methods. Moreover, our representations significantly improve vision-and-language navigation in continuous environments for both high-level and low-level action spaces, achieving new state-of-the-art results of 47% SR and 41% SPL on the test server.