Uni-Sight: An E2E Vision-Language-Action System Unifying Multi-View Alignment and Multi-Modal Fusion

Vision-Language-Action (VLA) systems are crucial for autonomous decision-making in embodied intelligence. While current systems have advanced the instruction-following capabilities, their limited spatial perception often leads to suboptimal performance for mobile manipulation tasks in unstructured environments. To address this challenge, we propose Uni-Sight, an end-to-end VLA system for robust mobile manipulation. Uni-Sight unifies decision-making, perception, and control through joint training, enabling synchronized cross-component optimization. Within the system, we introduce Latent Feature Aligner (LFA) that ensures accurate target localization by aligning multi-view data. Specifically, we develop Domain Transfer Policy (DTP), a hierarchical policy constrained by LiDAR-guided spatial priors, which ensures 3D spatial understanding with limited visual coverage. Extensive experiments on 20 real-world mobile manipulation tasks demonstrate the high task success rate and robust execution performance of Uni-Sight. Our Uni-Sight achieves a 3.04× the success rate of existing methods, and exhibits superior generalization in both long-horizon and zero-shot scenes. Code and dataset are publicly available at https://github.com/trantor2nd/Uni-Sight.