Progressively Learning Heterogeneous Skills in a Unified Latent Space
We propose HetSkills, a novel framework designed to progressively learn heterogeneous skills within a unified latent space for physics-based character control. The core idea is to treat this latent space as a shared executable interface, enabling seamless integration of skills learned from diverse data sources, supervision forms, and tasks. HetSkills begins by learning a tracking skill that establishes a strong foundation in motion control and creates a shared motion decoder, which can be reused across tasks without the need for retraining or separate controllers. To prevent the text-to-motion skill from exploiting shortcut pathways instead of learning language semantics, we introduce mot...
| Authors | Yue-Yi Zhang, Ming Gong, Linpu He et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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