  We present a framework that enables the discovery of diverse and
natural-looking motion strategies for athletic skills such as the high jump.
The strategies are realized as control policies for physics-based characters.
Given a task objective and an initial character configuration, the combination
of physics simulation and deep reinforcement learning (DRL) provides a suitable
starting point for automatic control policy training. To facilitate the
learning of realistic human motions, we propose a Pose Variational Autoencoder
(P-VAE) to constrain the actions to a subspace of natural poses. In contrast to
motion imitation methods, a rich variety of novel strategies can naturally
emerge by exploring initial character states through a sample-efficient
Bayesian diversity search (BDS) algorithm. A second stage of optimization that
encourages novel policies can further enrich the unique strategies discovered.
Our method allows for the discovery of diverse and novel strategies for
athletic jumping motions such as high jumps and obstacle jumps with no motion
examples and less reward engineering than prior work.
