Crowd navigation has received significant research attention in recent years, especially DRL-based methods. While single-robot crowd scenarios have dominated research, they offer limited applicability to real-world complexities. The heterogeneity of interaction among multiple agent categories, like in decentralized multi-robot pedestrian scenarios, are frequently disregarded. This "interaction blind spot" hinders generalizability and restricts progress towards robust navigation algorithms. In this paper, we propose a heterogeneous relational deep reinforcement learning(HeR-DRL), based on customised heterogeneous GNN, in order to improve navigation strategies in decentralized multi-robot crowd navigation. Firstly, we devised a method for constructing robot-crowd heterogenous relation graph that effectively simulates the heterogeneous pair-wise interaction relationships. We proposed a new heterogeneous graph neural network for transferring and aggregating the heterogeneous state information. Finally, we incorporate the encoded information into deep reinforcement learning to explore the optimal policy. HeR-DRL are rigorously evaluated through comparing it to state-of-the-art algorithms in both single-robot and multi-robot circle crowssing scenario. The experimental results demonstrate that HeR-DRL surpasses the state-of-the-art approaches in overall performance, particularly excelling in safety and comfort metrics. This underscores the significance of interaction heterogeneity for crowd navigation. The source code will be publicly released in https://github.com/Zhouxy-Debugging-Den/HeR-DRL.

翻译：摘要：人群导航近年来受到广泛研究关注，尤其基于深度强化学习(DRL)的方法。尽管单机器人人群场景主导了现有研究，但其对现实复杂场景的适用性有限。去中心化多机器人行人场景中多智能体类别间的异构交互作用常被忽视，这种"交互盲区"阻碍了泛化能力并限制了鲁棒导航算法的发展。本文提出基于自定义异构图神经网络的异构关系深度强化学习(HeR-DRL)，以提升去中心化多机器人人群导航策略。首先，我们设计了机器人-人群异构关系图构建方法，有效模拟异构成对交互关系；其次，提出新型异构图神经网络用于异构状态信息的传递与聚合；最后，将编码信息融入深度强化学习以探索最优策略。通过在单机器人和多机器人圆形穿越场景中与前沿算法进行严格对比评估，实验结果表明HeR-DRL在整体性能上超越现有方法，尤其在安全性与舒适性指标上表现突出，凸显了交互异构性对人群导航的重要性。源代码将公开发布于https://github.com/Zhouxy-Debugging-Den/HeR-DRL。