  Recent progress on physics-based character animation has shown impressive
breakthroughs on human motion synthesis, through imitating motion capture data
via deep reinforcement learning. However, results have mostly been demonstrated
on imitating a single distinct motion pattern, and do not generalize to
interactive tasks that require flexible motion patterns due to varying
human-object spatial configurations. To bridge this gap, we focus on one class
of interactive tasks -- sitting onto a chair. We propose a hierarchical
reinforcement learning framework which relies on a collection of subtask
controllers trained to imitate simple, reusable mocap motions, and a meta
controller trained to execute the subtasks properly to complete the main task.
We experimentally demonstrate the strength of our approach over different
non-hierarchical and hierarchical baselines. We also show that our approach can
be applied to motion prediction given an image input. A supplementary video can
be found at https://youtu.be/3CeN0OGz2cA.
