HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation
This addresses the gap between simulation and real-world applications in Embodied AI for researchers and developers, though it is incremental as it builds on existing simulators.
The paper tackles the lack of efficient simulators for crowd-aware visual navigation by introducing HabiCrowd, a benchmark that integrates crowd dynamics into photorealistic environments, achieving state-of-the-art collision avoidance and superior computational efficiency.
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/.