DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in
  Dynamic Environments
Ji Ma Hongming Dai Yao Mu Pengying Wu Hao Wang Xiaowei Chi Yang Fei Shanghang Zhang Chang Liu
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 https://DOZE-Dataset.github.io/.