GeoVLA: 視覚言語行動モデルに3D表現を導入
GeoVLA: Empowering 3D Representations in Vision-Language-Action Models
深度マップから点群を生成し、視覚言語モデルの埋め込みと統合することで、3D空間認識を強化したロボットマニピュレーション用VLAフレームワークを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Lin Sun, Bin Xie, Yingfei Liu, Hao Shi, Tiancai Wang, Jiale Cao
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models have emerged as a promising approach for enabling robots to follow language instructions and predict corresponding actions. However, current VLA models mainly rely on 2D visual inputs, neglecting the rich geometric information in the 3D physical world, which limits their spatial awareness and adaptability. In this paper, we present GeoVLA, a novel VLA framework that effectively integrates 3D information to advance robotic manipulation. It uses a vision-language model (VLM) to process images and language instructions,extracting fused vision-language embeddings. In parallel, it converts depth maps into point clouds and employs a customized point encoder, called Point Embedding Network, to generate 3D geometric embeddings independently. These produced embeddings are then concatenated and processed by our proposed spatial-aware action expert, called 3D-enhanced Action Expert, which combines information from different sensor modalities to produce precise action sequences. Through extensive experiments in both simulation and real-world environments, GeoVLA demonstrates superior performance and robustness. It achieves state-of-the-art results in the LIBERO and ManiSkill2 simulation benchmarks and shows remarkable robustness in real-world tasks requiring height adaptability, scale awareness and viewpoint invariance.
