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.

翻译：我们提出C$\cdot$ASE，一种高效且有效的框架，用于学习物理角色的条件对抗技能嵌入。该物理仿真角色能够学习多样化的技能库，同时通过直接操控待执行技能的方式提供可控性。C$\cdot$ASE将异构技能动作划分为包含同质样本的独立子集，用于训练底层条件模型以学习条件行为分布。基于技能条件的模仿学习在训练后自然赋予对角色技能的显式控制能力。训练过程中，我们引入焦点技能采样、骨架残差力与逐元素特征掩码，以平衡复杂度不同的多样化技能、缓解动态失配以实现敏捷运动掌握、并捕捉更泛化的行为特征。经训练后，条件模型可生成高度多样且逼真的技能，性能超越当前最优模型，并可在多种下游任务中复用。特别是，显式技能控制接口允许高层策略或用户通过指定技能需求来引导角色行为，我们证明了该方法对交互式角色动画具有显著优势。