任务控制下的复合运动学习

摘要

我们提出了一种用于物理模拟角色的复合和任务驱动运动控制的深度学习方法。与使用强化学习模仿全身运动的现有数据驱动方法不同，我们通过在类似GAN的设置中利用多个鉴别器，同时直接从多个参考运动中为特定身体部位学习解耦合的运动。在这个过程中，无需进行任何手动工作来生成用于学习的复合参考运动。相反，控制策略自行探索如何自动组合复合运动。我们进一步考虑多个特定任务奖励，并训练一个单一的、多目标的控制策略。为此，我们提出了一个新颖的多目标学习框架，自适应平衡来自多个来源和多个目标导向控制目标的不同运动的学习。此外，由于复合运动通常是更简单行为的增强，我们引入了一种高效的样本方法来逐步训练复合控制策略，其中我们将一个预先训练的策略作为元策略并训练一个合作策略，使其为新的复合任务进行调整。我们展示了我们的方法在涉及复合运动模仿和多目标导向控制的各种具有挑战性的多目标任务上的适用性。

English

We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforcement learning that imitate full-body motions, we learn decoupled motions for specific body parts from multiple reference motions simultaneously and directly by leveraging the use of multiple discriminators in a GAN-like setup. In this process, there is no need of any manual work to produce composite reference motions for learning. Instead, the control policy explores by itself how the composite motions can be combined automatically. We further account for multiple task-specific rewards and train a single, multi-objective control policy. To this end, we propose a novel framework for multi-objective learning that adaptively balances the learning of disparate motions from multiple sources and multiple goal-directed control objectives. In addition, as composite motions are typically augmentations of simpler behaviors, we introduce a sample-efficient method for training composite control policies in an incremental manner, where we reuse a pre-trained policy as the meta policy and train a cooperative policy that adapts the meta one for new composite tasks. We show the applicability of our approach on a variety of challenging multi-objective tasks involving both composite motion imitation and multiple goal-directed control.

任务控制下的复合运动学习

Composite Motion Learning with Task Control

摘要

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