https://arpspoof.github.io/project/jump/jump.html

Discovering Diverse Athletic Jumping Strategies

ACM SIGGRAPH 2021

 Zhiqi Yin ^1 Zeshi Yang ^1 Michiel van de Panne ^2 KangKang Yin ^1

 ^1 Simon Fraser University ^2 University of British Columbia  
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Abstract

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.

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