title: Composite Motion Learning with Task Control
summary: 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.
paper_authors._id: 64585d2eaa426bae5e07317f
paper_authors.name: Pei Xu
paper_authors._id: 64585d2eaa426bae5e073180
paper_authors.name: Xiumin Shang
paper_authors._id: 64585d2eaa426bae5e073181
paper_authors.name: Victor Zordan
paper_authors._id: 64585d2eaa426bae5e073182
paper_authors.name: Ioannis Karamouzas
paper_title: Composite Motion Learning with Task Control
paper_summary: 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.
paper_ai_summary: A novel deep learning framework uses multiple discriminators in a GAN-like setup to learn decoupled, composite motions from reference motions and train a multi-objective policy with adaptive balancing for various tasks.
paper_ai_keywords: motion control reinforcement learning GAN discriminators composite motions multi-objective learning adaptive balancing sample-efficient incremental training cooperative policy