DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments
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| Title: | DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments |
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| Authors: | Ji Ma, Hongming Dai, Yao Mu, Pengying Wu, Hao Wang, Xiaowei Chi, Yang Fei, Shanghang Zhang, Chang Liu |
| Source: | IEEE Robotics and Automation Letters. 9:7389-7396 |
| Publication Status: | Preprint |
| Publisher Information: | Institute of Electrical and Electronics Engineers (IEEE), 2024. |
| Publication Year: | 2024 |
| Subject Terms: | FOS: Computer and information sciences, Computer Science - Robotics, Data sets for robot learning, Computer Vision and Pattern Recognition (cs.CV), 11. Sustainability, Computer Science - Computer Vision and Pattern Recognition, Embodied AI, Zero-shot object navigation, Semantic scene understanding, Robotics (cs.RO), Data sets for robotic vision |
| Description: | Zero-Shot Object Navigation (ZSON) requires agents to autonomously locate and approach unseen objects in unfamiliar environments and has emerged as a particularly challenging task within the domain of Embodied AI. Existing datasets for developing ZSON algorithms lack consideration of dynamic obstacles, object attribute diversity, and scene texts, thus exhibiting noticeable discrepancies from real-world situations. To address these issues, we propose a Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments (DOZE) that comprises ten high-fidelity 3D scenes with over 18k tasks, aiming to mimic complex, dynamic real-world scenarios. Specifically, DOZE scenes feature multiple moving humanoid obstacles, a wide array of open-vocabulary objects, diverse distinct-attribute objects, and valuable textual hints. Besides, different from existing datasets that only provide collision checking between the agent and static obstacles, we enhance DOZE by integrating capabilities for detecting collisions between the agent and moving obstacles. This novel functionality enables the evaluation of the agents' collision avoidance abilities in dynamic environments. We test four representative ZSON methods on DOZE, revealing substantial room for improvement in existing approaches concerning navigation efficiency, safety, and object recognition accuracy. Our dataset can be found at https://DOZE-Dataset.github.io/. This version of the paper has been accepted for publication in IEEE Robotics and Automation Letters (RA-L) |
| Document Type: | Article |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2024.3426381 |
| DOI: | 10.48550/arxiv.2402.19007 |
| Access URL: | http://arxiv.org/abs/2402.19007 |
| Rights: | IEEE Copyright arXiv Non-Exclusive Distribution |
| Accession Number: | edsair.doi.dedup.....29d1205d8a44407ffc62e203c19d7fbe |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Zero-Shot Object Navigation (ZSON) requires agents to autonomously locate and approach unseen objects in unfamiliar environments and has emerged as a particularly challenging task within the domain of Embodied AI. Existing datasets for developing ZSON algorithms lack consideration of dynamic obstacles, object attribute diversity, and scene texts, thus exhibiting noticeable discrepancies from real-world situations. To address these issues, we propose a Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments (DOZE) that comprises ten high-fidelity 3D scenes with over 18k tasks, aiming to mimic complex, dynamic real-world scenarios. Specifically, DOZE scenes feature multiple moving humanoid obstacles, a wide array of open-vocabulary objects, diverse distinct-attribute objects, and valuable textual hints. Besides, different from existing datasets that only provide collision checking between the agent and static obstacles, we enhance DOZE by integrating capabilities for detecting collisions between the agent and moving obstacles. This novel functionality enables the evaluation of the agents' collision avoidance abilities in dynamic environments. We test four representative ZSON methods on DOZE, revealing substantial room for improvement in existing approaches concerning navigation efficiency, safety, and object recognition accuracy. Our dataset can be found at https://DOZE-Dataset.github.io/.<br />This version of the paper has been accepted for publication in IEEE Robotics and Automation Letters (RA-L) |
|---|---|
| ISSN: | 23773774 |
| DOI: | 10.1109/lra.2024.3426381 |