  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.
