Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
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 text
pretraining. However, 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 defective estimation. In contrast, our work
introduces a plug-and-play module that implicitly injects 3D geometry features
into VLA models by leveraging an off-the-shelf visual geometry foundation
models. We design five spatially challenging tasks that require precise spatial
understanding ability to validate effectiveness of our method. Extensive
evaluations show that our method significantly improves the performance of
state-of-the-art VLA models across diverse scenarios.
