統一潜在空間における異種スキルの段階的学習
Progressively Learning Heterogeneous Skills in a Unified Latent Space
物理ベースのキャラクター制御において、多様なデータ・教師信号・タスクから得た異種スキルを統一潜在空間で段階的に学習するフレームワークHetSkillsを提案。モーション追跡、テキストからのモーション生成、モーション補完、下流タスク適応で有効性を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Yue-Yi Zhang, Ming Gong, Linpu He, Wei-Shi Zheng, Zhilin Zhao
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
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.