REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments
Open accessAbstract
One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language. Overcoming this challenge requires the ability to perform a wide variety of complex tasks in response to multifarious instructions from humans. In the hope that it might drive progress towards more flexible and powerful human interactions with robots, we propose a dataset of varied and complex robot tasks, described in natural language, in terms of objects visible in a large set of real images. Given an instruction, success requires navigating through a previously-unseen environment to identify an object. This represents a practical challenge, but one that closely reflects one of the core visual problems in robotics. Several state-of-the-art vision-and-language navigation, and referring-expression models are tested to verify the difficulty of this new task, but none of them show promising results because there are many fundamental differences between our task and previous ones. A novel Interactive Navigator-Pointer model is also proposed that provides a strong baseline on the task. The proposed model especially achieves the best performance on the unseen test split, but still leaves substantial room for improvement compared to the human performance. Repository: https://github.com/YuankaiQi/REVERIE.
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SimpleBEV adopts a BEV-based multi-scale fusion strategy, strengthening depth perception and maintaining stable overall detection performance.
“SimpleBEV [260], adopting a BEV-based multi-scale fusion strategy, strengthens depth perception and maintains stable overall detection performance.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [260]
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“Moreover, we validate that an agent trained with augmented instructions generated by a simple LSTM-based model [72] can achieve good performance on multiple navigation tasks [10, 60, 73].”
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- Authors
- Qi, Y., Wu, Q., Anderson, P., Wang, X., Wang, W. Y., Shen, C., & van den Hengel, A.
- Affiliations
- Harbin Institute of Technology (CN)
- Australian Centre for Robotic Vision (AU)
- Georgia Institute of Technology (US)
- University of California, Santa Barbara (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 9979-9988
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034578524
- Cited by
- 331
- References
- 34
- Citations by year
- 2026: 49 · 2025: 67 · 2024: 59 · 2023: 60 · 2022: 38 · 2021: 39 · 2020: 16 · 2019: 3
- FWCI
- 11.68
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- embodied navigation, human-robot interaction, natural language instructions, vision-and-language navigation, unknown environments, indoor environment
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