RGB-X multimodal vision tasks present a highly promising approach to enhancing model performance in complex visual conditions. Existing multimodal frameworks are based on either the symmetric parallel network of feature fusion or the shared network of input fusion. However, parallel networks suffer from uncontrollable parameters and imbalanced optimization across modal branches, while shared networks often lead to a lack of diversity in gradient optimization. To address these challenges, we propose the LoRA-driven Multimodal Extractor (LoME), following a comprehensive analysis of existing multimodal frameworks. The low-rank properties of modal adapters for LoME ensure controllable growth in model parameters as the number of modalities increases. The dynamic parameter fusion between adapters and the shared feature extractor decouples gradient optimization directions, effectively mitigating imbalances caused by multimodal data biases while preserving complementary features. Moreover, we employ a training strategy based on dynamic rank allocation to reduce computational overhead and enhance modal diversity expression. We validate the effectiveness and generalizability of LoME across three multimodal vision tasks. LoME achieves superior performance compared to previous state-of-the-art methods on multiple datasets. For example, on the DroneVehicle dataset, our method achieves a 10.4% improvement in accuracy compared to the SOTA method, while the parameter overhead is reduced to 23% of the previous network (44.63M). The code has been open-sourced at https://github.com/zyszxhy/LoME.

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.