用博弈论模拟人机交互，让机器人学会避障与协作。
Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors
- 每个智能体基于估计构建虚拟合作博弈，模拟人类互动行为。
- 可配置生成多样真实交互模式，适用于复杂场景测试。
- 提供类Gym环境，便于学习和评估导航算法。
在需兼顾避障与协作的复杂场景中，如室内空间，机器人导航面临重大挑战。与静态或可预测障碍物不同，人类行为具有内在复杂性和不可预测性，源于与其他智能体的动态交互。现有仿真工具常无法充分建模此类反应式与协作性行为，阻碍了鲁棒社交导航策略的发展。本文提出一种基于分布式势博弈的新框架，用于模拟高度互动场景中的人类行为。每个智能体根据自身估计，构想与其他智能体之间的虚拟合作博弈。我们证明该方法可在多种场景中以可配置方式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，利用该交互智能体模型，支持交互导航算法的学习与评估。
原文摘要 · Abstract (English)
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.
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