We present a method for reproducing complex multi-character interactions for physically simulated humanoid characters using deep reinforcement learning. Our method learns control policies for characters that imitate not only individual motions, but also the interactions between characters, while maintaining balance and matching the complexity of reference data. Our approach uses a novel reward formulation based on an interaction graph that measures distances between pairs of interaction landmarks. This reward encourages control policies to efficiently imitate the character's motion while preserving the spatial relationships of the interactions in the reference motion. We evaluate our method on a variety of activities, from simple interactions such as a high-five greeting to more complex interactions such as gymnastic exercises, Salsa dancing, and box carrying and throwing. This approach can be used to ``clean-up'' existing motion capture data to produce physically plausible interactions or to retarget motion to new characters with different sizes, kinematics or morphologies while maintaining the interactions in the original data.

翻译：我们提出了一种基于深度强化学习的物理仿真拟人角色复杂多人交互再现方法。该方法学习角色控制策略，不仅模仿个体运动，还模仿角色间的交互行为，同时保持平衡并匹配参考数据的复杂度。我们采用基于交互图的新型奖励公式，该公式通过测量交互关键点对之间的距离，鼓励控制策略在高效模仿角色运动的同时，保留参考动作中交互的空间关系。我们在多种活动场景中评估了该方法，涵盖从击掌问候等简单交互到体操练习、萨尔萨舞、箱体搬运与抛掷等复杂交互。该技术可用于对现有动作捕捉数据进行“提纯”，生成物理上合理的交互动作，或在不破坏原始数据交互特征的前提下，将运动重定向至具有不同体型、运动学特征或形态特征的新角色。