论文标题
人类机器人平行游戏中NASH平衡推断的贝叶斯框架
A Bayesian Framework for Nash Equilibrium Inference in Human-Robot Parallel Play
论文作者
论文摘要
我们考虑与人类和机器人共享的工作空间场景，以实现独立目标，称为平行游戏。我们将它们建模为通用游戏，并构建一个框架，该框架利用NASH平衡解决方案概念来考虑两种代理在计划时的互动效果。在这些任务中，我们发现多个帕累托最佳平衡。我们假设人们通过基于社会规范及其个性选择平衡来采取行动。为了启用协调，我们使用包含这两个因素的概率模型来推断在线均衡在线，并使用它来选择机器人的动作。我们将方法应用于涉及机器人和模拟人类的近距离选择任务，具有三种潜在的行为 - 防御，自私和遵守规范。我们表明，使用贝叶斯方法来推断平衡使机器人能够完成任务的碰撞数量少于一半，同时还可以减少任务执行时间与最佳基线相比。我们还与人类参与者进行了一项研究，该研究与其他人或不同的机器人药物相互作用，并观察到我们所提出的方法的执行类似于人类平行的游戏相互作用。该代码可从https://github.com/shray/bayes-nash获得
We consider shared workspace scenarios with humans and robots acting to achieve independent goals, termed as parallel play. We model these as general-sum games and construct a framework that utilizes the Nash equilibrium solution concept to consider the interactive effect of both agents while planning. We find multiple Pareto-optimal equilibria in these tasks. We hypothesize that people act by choosing an equilibrium based on social norms and their personalities. To enable coordination, we infer the equilibrium online using a probabilistic model that includes these two factors and use it to select the robot's action. We apply our approach to a close-proximity pick-and-place task involving a robot and a simulated human with three potential behaviors - defensive, selfish, and norm-following. We showed that using a Bayesian approach to infer the equilibrium enables the robot to complete the task with less than half the number of collisions while also reducing the task execution time as compared to the best baseline. We also performed a study with human participants interacting either with other humans or with different robot agents and observed that our proposed approach performs similar to human-human parallel play interactions. The code is available at https://github.com/shray/bayes-nash
