C$\cdot$ASE: Learning Conditional Adversarial Skill Embeddings for
  Physics-based Characters
Zhiyang Dou Xuelin Chen Qingnan Fan Taku Komura Wenping Wang
We present C$\cdot$ASE, an efficient and effective framework that learns
conditional Adversarial Skill Embeddings for physics-based characters. Our
physically simulated character can learn a diverse repertoire of skills while
providing controllability in the form of direct manipulation of the skills to
be performed. C$\cdot$ASE divides the heterogeneous skill motions into distinct
subsets containing homogeneous samples for training a low-level conditional
model to learn conditional behavior distribution. The skill-conditioned
imitation learning naturally offers explicit control over the character's
skills after training. The training course incorporates the focal skill
sampling, skeletal residual forces, and element-wise feature masking to balance
diverse skills of varying complexities, mitigate dynamics mismatch to master
agile motions and capture more general behavior characteristics, respectively.
Once trained, the conditional model can produce highly diverse and realistic
skills, outperforming state-of-the-art models, and can be repurposed in various
downstream tasks. In particular, the explicit skill control handle allows a
high-level policy or user to direct the character with desired skill
specifications, which we demonstrate is advantageous for interactive character
animation.