Evo-0: 暗黙的な空間理解を備えた視覚-言語-行動モデル
Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding
既存の視覚基盤モデルを活用し、RGB画像のみから3D幾何特徴を暗黙的に取り込むプラグアンドプレイモジュールを提案し、VLAモデルの空間理解能力を向上させた。
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: Tao Lin, Gen Li, Yilei Zhong, Yanwen Zou, Yuxin Du, Jiting Liu, Encheng Gu, Bo Zhao
分類: cs.RO, cs.CV
原文アブストラクト
Vision-Language-Action (VLA) models have emerged as a promising framework for enabling generalist robots capable of perceiving, reasoning, and acting in the real world. These models usually build upon pretrained Vision-Language Models (VLMs), which excel at semantic understanding due to large-scale image and text pretraining. However, existing VLMs typically lack precise spatial understanding capabilities, as they are primarily tuned on 2D image-text pairs without 3D supervision. To address this limitation, recent approaches have incorporated explicit 3D inputs such as point clouds or depth maps, but this necessitates additional depth sensors or pre-trained depth estimation models, which may yield defective results. In contrast, our work introduces a plug-and-play module that implicitly incorporates 3D geometry features into VLA models by leveraging an off-the-shelf visual geometry foundation model. This integration provides the model with depth-aware visual representations, improving its ability to understand the geometric structure of the scene and the spatial relationships among objects from RGB images alone. We evaluate our method on a set of spatially challenging tasks in both simulation and the real world. Extensive evaluations show that our method significantly improves the performance of state-of-the-art VLA models across diverse scenarios.
