Target-Driven Structured Transformer Planner for Vision-Language Navigation
Open accessAbstract
Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions. For the agent, inferring the long-term navigation target from visual-linguistic clues is crucial for reliable path planning, which, however, has rarely been studied before in literature. In this article, we propose a Target-Driven Structured Transformer Planner (TD-STP) for long-horizon goal-guided and room layout-aware navigation. Specifically, we devise an Imaginary Scene Tokenization mechanism for explicit estimation of the long-term target (even located in unexplored environments). In addition, we design a Structured Transformer Planner which elegantly incorporates the explored room layout into a neural attention architecture for structured and global planning. Experimental results demonstrate that our TD-STP substantially improves previous best methods' success rate by 2% and 5% on the test set of R2R and REVERIE benchmarks, respectively. Our code is available at https://github.com/YushengZhao/TD-STP.
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CVDN provides 2,050 human dialogs simulating scenarios where agents request assistance through natural language interactions during navigation.
“CVDN [124] provides 2,050 human dialogs simulating scenarios where agents request assistance through natural language interactions during navigation.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [124]
Applying CLIP ViT-H/14 and EnvEdit reduces the generalization gap between seen and unseen environments to less than 1%.
“and reducing the longlasting generalization gap between seen and unseen environments [86] to less than 1%.”
From Scaling Data Generation in Vision-and-Language Navigation (2023) · cites this paper as [86]
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- Authors
- Zhao, Y., Chen, J., Gao, C., Wang, W., Yang, L., Ren, H., Xia, H., & Liu, S.
- Affiliations
- Beihang University (CN)
- University of Technology Sydney (AU)
- Meituan (China) (CN)
- Venue
- Proceedings of the 30th ACM International Conference on Multimedia
- Publisher
- ACM
- Published
- 2022-10-10
- Pages
- 4194-4203
- OpenAlex
- W4304097971
- Cited by
- 54
- References
- 38
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 21 · 2023: 16
- FWCI
- 4.53
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China (62122010,61876177)
- Key Research and Development Program of Zhejiang Province (2022C01082)
- ARC DECRA (DE220101390)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- vision-and-language navigation, embodied agents, natural language instructions
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