StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision

Understand

While Vision-Language-Action (VLA) models excel in generalist manipulation, they often lack fine-grained spatial awareness and show limited viewpoint robustness.

• This limitation largely stems from the reliance on pretrained RGB encoders, which lack explicit geometric cues and prioritize semantic alignment over geometric representation.
• We argue that effective visual representations for VLA models must jointly encode both semantic and geometric information.
• In this paper, we introduce StereoVLA, the first VLA model to incorporate rich geometric cues from large-scale synthetic stereo data.