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arXiv · 2608.23258

Progressively Learning Heterogeneous Skills in a Unified Latent Space

Yue-Yi Zhang·Ming Gong·Linpu He·Wei-Shi Zheng·Zhilin Zhao
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Abstract

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 motion intuition distillation to ground text-to-motion generation in language semantics and a task-guidance module that dynamically adjusts actions based on high-level language instructions. This enables HetSkills to preserve natural motion while continuously expanding its skill repertoire, making it highly adaptable for long-horizon tasks. Experimental results demonstrate the effectiveness in motion tracking, text-to-motion generation, motion completion, and downstream task adaptation, achieving impressive success rates even under challenging conditions.

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Yue-Yi Zhang, Ming Gong, Linpu He, Wei-Shi Zheng, Zhilin Zhao. 2026-08-24. Progressively Learning Heterogeneous Skills in a Unified Latent Space. https://arxiv.org/abs/2608.23258

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