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 discrepancy 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 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 could be found at this https URL

零射对象导航 (ZSON) 需要在陌生环境中自主定位和接近看不见的物体，它是体验智能领域中的一项特别具有挑战性的任务。我们提出了一个在动态环境中进行开放词汇的零射对象导航数据集 (DOZE)，包括十个高保真度的 3D 场景及超过 18,000 个任务，旨在模拟复杂的实际情景。通过在 DOZE 场景中引入多个移动人形障碍物、大量的开放词汇物体、具有多样属性的物体和有价值的文本提示，我们解决了现有数据集在动态环境中的冲突检测方面的局限性。我们在 DOZE 上测试了四种有代表性的 ZSON 方法，揭示了现存方法在导航效率、安全性和物体识别准确性方面还有很大改进空间。