Discovering Diverse Athletic Jumping Strategies
This work addresses the challenge of creating realistic and varied motion strategies in animation and robotics, though it is incremental as it builds on existing DRL and VAE methods.
The authors tackled the problem of generating diverse and natural-looking athletic jumping motions for physics-based characters without using motion examples, achieving novel strategies for high jumps and obstacle jumps with reduced reward engineering.
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.