HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation

• URL: http://arxiv.org/abs/2306.11377v2
• Date: Mon, 29 Jul 2024 13:46:35 GMT
• Title: HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation
• Authors: An Dinh Vuong, Toan Tien Nguyen, Minh Nhat VU, Baoru Huang, Dzung Nguyen, Huynh Thi Thanh Binh, Thieu Vo, Anh Nguyen,
• Abstract summary: We introduce HabiCrowd, the first standard benchmark for crowd-aware visual navigation. Our proposed human dynamics model achieves state-of-the-art performance in collision avoidance. We leverage HabiCrowd to conduct several comprehensive studies on crowd-aware visual navigation tasks and human-robot interactions.
• Score: 8.484737966013059
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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/.

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