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

Shengliang Deng, Mi Yan, Yixin Zheng, Jiayi Su, Wenhao Zhang, Xiaoguang Zhao, Heming Cui, Zhizheng Zhang, He Wang

Abstract: Stereo cameras closely mimic human binocular vision, providing rich spatial cues critical for precise robotic manipulation. Despite their advantage, the adoption of stereo vision in vision-language-action models (VLAs) remains underexplored. In this work, we present StereoVLA, a VLA model that leverages rich geometric cues from stereo vision. We propose a novel Geometric-Semantic Feature Extraction module that utilizes vision foundation models to extract and fuse two key features: 1) geometric features from subtle stereo-view differences for spatial perception; 2) semantic-rich features from the monocular view for instruction following. Additionally, we propose an auxiliary Interaction-Region Depth Estimation task to further enhance spatial perception and accelerate model convergence. Extensive experiments show that our approach outperforms baselines by a large margin in diverse tasks under the stereo setting and demonstrates strong robustness to camera pose variations.

External IDs:dblp:journals/corr/abs-2512-21970