  The field of physics-based animation is gaining importance due to the
increasing demand for realism in video games and films, and has recently seen
wide adoption of data-driven techniques, such as deep reinforcement learning
(RL), which learn control from (human) demonstrations. While RL has shown
impressive results at reproducing individual motions and interactive
locomotion, existing methods are limited in their ability to generalize to new
motions and their ability to compose a complex motion sequence interactively.
In this paper, we propose a physics-based universal neural controller (UniCon)
that learns to master thousands of motions with different styles by learning on
large-scale motion datasets. UniCon is a two-level framework that consists of a
high-level motion scheduler and an RL-powered low-level motion executor, which
is our key innovation. By systematically analyzing existing multi-motion RL
frameworks, we introduce a novel objective function and training techniques
which make a significant leap in performance. Once trained, our motion executor
can be combined with different high-level schedulers without the need for
retraining, enabling a variety of real-time interactive applications. We show
that UniCon can support keyboard-driven control, compose motion sequences drawn
from a large pool of locomotion and acrobatics skills and teleport a person
captured on video to a physics-based virtual avatar. Numerical and qualitative
results demonstrate a significant improvement in efficiency, robustness and
generalizability of UniCon over prior state-of-the-art, showcasing
transferability to unseen motions, unseen humanoid models and unseen
perturbation.
