Pointing-VLA: 視覚言語行動操作のための型付き空間接地インターフェース
Pointing-VLA: Typed Spatial Grounding Interfaces for Vision-Language-Action Manipulation
VLAモデルの空間接地をテキスト座標ではなく型付きヘッドで直接予測し、実行契約によりPICKとPLACEを分離。Bridge/WidowXでSOTAを達成し、実ロボット成功率を52.7%から80.7%に向上させた。
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著者: Xiwen Chen, Zelin Li, Zhiruo Zhou, Huiming Chen, Chenwei Wang, Xiaojun Zhu
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Vision-language-action (VLA) models often expose spatial grounding through autoregressive text coordinates or opaque action tokens, creating brittle interfaces between multimodal reasoning and robot execution. We present Pointing-VLA, a typed hidden-state spatial readout built on Embodied-R1. Geometry-specific heads predict normalized points, object-functional grounding (OFG) heatmaps, and visual trajectories without serializing geometry as text. For the evaluated Bridge/WidowX and physical pick-place deployments, an explicit execution contract assigns PICK to source-conditioned OFG and PLACE to Pointing, providing direct stage-aligned spatial targets. Pointing-VLA achieves SOTA performance on Bridge/WidowX, averaging 72.9\% across the evaluated four-task set without Bridge-specific finetuning under collision-enabled CuRobo execution. Pointing and OFG show complementary strengths across native and cross-dataset evaluations. The OFG/contact readout transfers to NORA-1.5, preserving or improving success while reducing recorded controller time by more than 20$\times$; typed heads are also 6.68--6.90$\times$ faster than Embodied-R1 text decoding on a shared external suite. When integrated as spatial guidance for a $π_{0.5}$ action policy, Pointing-VLA raises autonomous real-robot success from 52.7\% to 80.7\% across three visual contexts. These results establish typed spatial readouts as an efficient, inspectable interface between embodied reasoning and robot execution.
