GraspALL: Adaptive Structural Compensation from Illumination Variation for Robotic Garment Grasping in Any Low-Light Conditions
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Zhong, Haifeng Han, Wenshuo Wang, Zhouyu Feng, Runyang Tang, Fan Lee, Tong-Yee Fan, Zipei Wu, Ruihai Wang, Yuran Dong, Hao Chen, Hechang Chang, Hyung Jin Gao, Yixing |
| author_facet | Zhong, Haifeng Han, Wenshuo Wang, Zhouyu Feng, Runyang Tang, Fan Lee, Tong-Yee Fan, Zipei Wu, Ruihai Wang, Yuran Dong, Hao Chen, Hechang Chang, Hyung Jin Gao, Yixing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14789 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | 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 Wang, Yuran Dong, Hao Chen, Hechang Chang, Hyung Jin Gao, Yixing Robotics 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. |
| title | GraspALL: Adaptive Structural Compensation from Illumination Variation for Robotic Garment Grasping in Any Low-Light Conditions |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.14789 |