发表机构
Sun Yat-sen University（中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出HetSkills框架，通过将隐空间作为共享接口实现异构技能渐进式学习，引入运动直觉蒸馏与任务指导模块，在多类运动任务中表现出色，适配长程任务。
AI 中文摘要
我们提出HetSkills，这是一种旨在在统一隐空间中为基于物理的角色控制渐进式学习异构技能的新型框架。核心思想是将该隐空间视为共享可执行接口，实现从不同数据源、监督形式和任务中学习的技能的无缝集成。HetSkills首先学习跟踪技能，该技能在运动控制中建立坚实基础并创建共享运动解码器，可跨任务复用，无需重新训练或单独控制器。为防止文本到运动技能利用捷径通路而非学习语言语义，我们引入运动直觉蒸馏以将文本到运动生成锚定在语言语义中，并引入任务指导模块，该模块可基于高级语言指令动态调整动作。这使HetSkills能够保留自然运动，同时持续扩展其技能库，使其对长程任务具有高度适应性。实验结果证明了其在运动跟踪、文本到运动生成、运动补全及下游任务适配中的有效性，即便在挑战性条件下也能实现出色的成功率。
英文摘要
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.
Comments23 pages, 18 figures
