Mind-VLA: 指示を考慮した空間表現アライメントによる視覚言語行動モデル
Mind-VLA: Instruction-Aware Spatial Representation Alignment for Vision-Language-Action Models
言語指示で指定された対象物体の3D情報に基づいてVLAモデルの表現を整列させる手法を提案し、細かい操作や遮蔽タスクでの性能を大幅に向上させた。
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著者: Xingyu Ding, Yuzhong Zhao, Yang Wu, Chaoyang Zhao, Chunhai Zhao, Yifan Zhang, Jian Cheng
分類: cs.RO
原文アブストラクト
Recent Vision-Language-Action (VLA) methods improve generalization by aligning their representations with 3D scene geometry. However, these methods are fundamentally instruction-agnostic: the representations align the entire scene uniformly, neglecting the 3D geometry of the specific target object designated by the language instruction. This causes failures on fine-grained manipulation and target occlusion tasks, where success depends on accurate 3D understanding of the target object rather than the entire scene. To address this, we present Mind-VLA, an instruction-aware spatial representation alignment method for VLA models. Specifically, Mind-VLA first obtains the target object specified by the language instruction, then prepares its target-object tri-view and extracts the corresponding VAE and VGGT features. Finally, the latent representation of the VLA model is aligned with these features to enable instruction-aware 3D understanding. Mind-VLA reaches 93.9% on LIBERO and 4.47 on CALVIN with a compact 345M-parameter backbone. On real-robot tasks with target occlusion, Mind-VLA reaches 54% average success, outperforming the best-performing instruction-agnostic method in real-robot comparison by 32 percentage points. Code will be publicly available.