3D空間理解を蒸留・知識ルーティングでVLAに付与するSPARK-VLA
Spatial Representation Distillation and Knowledge Routing for Vision-Language-Action Models
推論時に外部3D入力や専門家を必要とせず、凍結した3D専門家からLoRA視覚枝へ空間表現を蒸留し、軽量ルータで行動に関連する視覚トークンを選択するVLAフレームワークを提案。
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著者: Jungin Park, Chaoran Zhu, Changjae Oh, Kwanghoon Sohn
分類: cs.RO
原文アブストラクト
Understanding three-dimensional physical structure has become essential for vision-language-action models (VLAs). However, existing approaches either rely on explicit geometric inputs at inference time or adopt a single visual pathway, potentially compromising the visual-semantic knowledge inherited from pre-trained models. We present SPARK-VLA, Spatial Representation distillation and Knowledge routing for VLAs, a preserve-specialize-route framework for spatially adapting VLAs without requiring external 3D inputs or experts at inference. SPARK-VLA consists of two key components: (1) spatial representation distillation, which transfers 3D understanding from a frozen 3D expert to a LoRA-adapted visual branch by aligning attention responses while preserving the original visual pathway, and (2) a lightweight knowledge router, which selectively forwards action-relevant visual tokens to the language model using routing supervision derived from action-loss gradients. Across LIBERO, LIBERO-Plus, CALVIN, and real-world manipulation, SPARK-VLA achieves strong performance with only a 0.5B language backbone and approximately 53M trainable parameters, providing an effective and parameter-efficient approach to 3D-aware VLA learning. Code is available at https://github.com/park-jungin/spark-vla.