Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance and collaborative interaction, such as indoor spaces. Unlike static or predictably moving obstacles, human behavior is inherently complex and unpredictable, stemming from dynamic interactions with other agents. Existing simulation tools frequently fail to adequately model such reactive and collaborative behaviors, impeding the development and evaluation of robust social navigation strategies. This paper introduces a novel framework utilizing distributed potential games to simulate human-like interactions in highly interactive scenarios. Within this framework, each agent imagines a virtual cooperative game with others based on its estimation. We demonstrate this formulation can facilitate the generation of diverse and realistic interaction patterns in a configurable manner across various scenarios. Additionally, we have developed a gym-like environment leveraging our interactive agent model to facilitate the learning and evaluation of interactive navigation algorithms.

翻译：在复杂场景中与人类智能体交互对机器人导航提出了重大挑战，特别是在需要同时避免碰撞和协作交互的环境（如室内空间）中。与静态或可预测移动的障碍物不同，人类行为本质上是复杂且不可预测的，这源于与其他智能体的动态交互。现有仿真工具往往无法充分模拟此类反应性和协作性行为，从而阻碍了鲁棒社交导航策略的开发与评估。本文提出一种新颖框架，利用分布式势能博弈在高度交互场景中模拟类人交互。在该框架中，每个智能体基于其自身估计，想象与其他智能体进行虚拟合作博弈。我们证明该公式能够以可配置的方式，在不同场景中促进生成多样且真实的交互模式。此外，我们开发了一个类似gym的环境，利用我们的交互智能体模型，以促进交互式导航算法的学习与评估。