GraspALL: Adaptive Structural Compensation from Illumination Variation for Robotic Garment Grasping in Any Low-Light Conditions
Zhong, Haifeng, Han, Wenshuo, Wang, Zhouyu, Feng, Runyang, Tang, Fan, Lee, Tong-Yee, Fan, Zipei, Wu, Ruihai 等 13 位作者
Achieving accurate garment grasping under dynamically changing illumination is crucial for all-day operation of service robots. However, the reduced illumination in low-light scenes severely degrades garment structural features, leading to a significant drop in grasping robustness. Existing methods typically enhance RGB features by exploiting the illumination-invariant properties of non-RGB modalities, yet they overlook the varying dependence on non-RGB features under varying lighting conditions, which can introduce misaligned non-RGB cues and thereby weaken the model's adaptability to illumination changes when utilizing multimodal information. To address this problem, we propose GraspALL, an illumination-structure interactive compensation model. The innovation of GraspALL lies in encoding continuous illumination changes into quantitative references to guide adaptive feature fusion between RGB and non-RGB modalities according to varying lighting intensities, thereby generating illumination-consistent grasping representations. Experiments on the self-built garment grasping dataset demonstrate that GraspALL improves grasping accuracy by 32-44% over baselines under diverse illumination conditions. The code is available at https://github.com/Zhonghaifeng6/GraspALL
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表