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.

我们考虑人类和机器人为实现独立目标而行动的共享工作空间场景，称为平行游戏。我们将这些建模为一般和游戏，并构建了一个框架，该框架利用纳什均衡解决方案的概念来考虑两个代理在规划时的交互效果。我们在这些任务中发现了多个帕累托最优均衡。我们假设人们通过选择基于社会规范和个性的均衡来行动。为了实现协调，我们使用包含这两个因素的概率模型在线推断均衡，并使用它来选择机器人的动作。我们将我们的方法应用于一项近距离拾放任务，该任务涉及机器人和具有三种潜在行为的模拟人——防御性、自私和遵循规范。我们表明，与最佳基线相比，使用贝叶斯方法来推断平衡使机器人能够以不到一半的碰撞次数完成任务，同时还减少了任务执行时间。我们还对人类参与者与其他人类或不同机器人代理交互进行了一项研究，并观察到我们提出的方法与人与人平行游戏交互的表现相似。