Diversity-Aware Crowd Model for Robust Robot Navigation in Human Populated Environment

Robot navigation in human-populated environments poses challenges due to the diversity of human behaviors and the unpredictability of human paths. However, existing Reinforcement Learning (RL)-based methods often rely on simulators that lack sufficient diversity in human behavior, resulting in navigation policies that overfit specific human behavior and perform poorly in unseen environments. To address this, we propose a diversity-aware crowd model based on RL, employing Constrained Variational Exploration (VE) with a Mutual Information (MI)-based auxiliary reward to capture fine-grained behavioral diversity. The proposed model leverages a Centralized Training Decentralized Execution (CTDE) paradigm, which ensures stable exploration under multi-agent settings. Using the proposed diversity-aware model for training, we obtain robust robot navigation policies capable of handling diverse unseen scenarios. Extensive simulation and real-world experiments demonstrate the superior performance of our approach in achieving diverse crowd behaviors and enhancing robot navigation robustness. These findings highlight the potential of our method to advance safe and efficient robot operations in complex dynamic environments.