HabiCrowd：面向人群感知视觉导航的高性能模拟器
HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation
AI总结：
本文提出HabiCrowd，首个面向人群感知视觉导航的标准基准，通过将人群动态模型与多样化人类设置集成到逼真环境中，解决了现有3D模拟器在人类动态结合和计算效率上的不足，并在避碰性能和计算效率上达到领先水平。
摘要：
视觉导航是具身智能（E-AI）的基础方面，在过去几年中得到了广泛研究。尽管已引入许多3D模拟器来支持视觉导航任务，但鲜有研究致力于结合人类动态，从而造成了模拟与现实世界应用之间的差距。此外，当前结合人类动态的3D模拟器存在若干局限性，特别是在计算效率方面，而这正是E-AI模拟器的承诺所在。为克服这些不足，我们引入了HabiCrowd，这是首个面向人群感知视觉导航的标准基准，它将人群动态模型与多样化的人类设置集成到逼真的环境中。实证评估表明，我们提出的人类动态模型在避碰方面达到了最先进的性能，同时与同类模型相比展现出卓越的计算效率。我们利用HabiCrowd对人群感知视觉导航任务和人机交互进行了几项全面研究。源代码和数据可在 https://habicrowd.github.io/ 找到。
英文摘要：
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 https://habicrowd.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
