目录Part I: Theoretical Foundations and Methodology第一部分：理论基础与方法论1.1 Architectural Overview of the Generalist-Specialist Framework1.1.1 Paradigm Motivation and Design Philosophy1.1.2 Two-Stage Training Paradigm1.2 Generalist Phase: Large-Scale Multi-Task Pre-training1.2.1 MoCap Data Integration and Motion Prior1.2.2 Reinforcement Learning for Dynamics Alignment1.2.3 Latent Motion Prior Characterization1.3 Specialist Phase: Task-Specific Fine-tuning1.3.1 Policy Distillation and Constraint Preservation1.3.2 Expressiveness Optimization1.3.3 Stability Guarantee Mechanisms1.4 Latent Space Manipulation and Interpolation1.4.1 Semantic Structure of the Latent Manifold1.4.2 Conditional Generation via Latent Interpolation1.4.3 Trajectory Planning in Latent SpaceAlgorithm Original Text / 算法原文Algorithm 1: Generalist Phase Training with MoCap and RL算法 1：结合动捕数据与强化学习的通才阶段训练Algorithm 2: Specialist Phase Fine-tuning with Stability Constraints算法 2：带有稳定性约束的专才阶段微调Algorithm 3: Latent Space Interpolation for Continuous Behavior Generation算法 3：用于连续行为生成的潜在空间插值Part III: Implementation and System IntegrationScript 1: Generalist Phase Training ImplementationScript 2: Specialist Phase Fine-tuning ImplementationScript 3: Latent Space Interpolation and Behavior GenerationScript 4: Unified Visualization and Analysis DashboardPart I: Theoretical Foundations and Methodology第一部分：理论基础与方法论1.1 Architectural Overview of the Generalist-Specialist Framework