Visual navigation, a foundational aspect of Embodied AI (E-AI), has been
significantly studied in the past few years. While many 3D simulators have been
introduced to support visual navigation tasks, scarcely works have been
directed towards combining human dynamics, creating the gap between simulation
and real-world applications. Furthermore, current 3D simulators incorporating
human dynamics have several limitations, particularly in terms of computational
efficiency, which is a promise of E-AI simulators. To overcome these
shortcomings, we introduce HabiCrowd, the first standard benchmark for
crowd-aware visual navigation that integrates a crowd dynamics model with
diverse human settings into photorealistic environments. Empirical evaluations
demonstrate that our proposed human dynamics model achieves state-of-the-art
performance in collision avoidance, while exhibiting superior computational
efficiency compared to its counterparts. We leverage HabiCrowd to conduct
several comprehensive studies on crowd-aware visual navigation tasks and
human-robot interactions. The source code and data can be found at
this https URL

本文介绍了 HabiCrowd，这是第一个将人群动态模型与不同人物设定集成到逼真环境中的视觉导航基准，实证评估表明，我们提出的人体动力学模型在避碰方面具有卓越的计算效率和最新技术性能，利用 HabiCrowd 进行了人群感知视觉导航任务和人机交互方面的几项研究。