GraspALL：面向任意低光照条件下机器人衣物抓取的自适应光照变化结构补偿
GraspALL: Adaptive Structural Compensation from Illumination Variation for Robotic Garment Grasping in Any Low-Light Conditions
针对低光照下衣物抓取任务，提出将连续光照变化编码为定量参考，以指导RGB与非RGB模态的自适应融合，生成光照一致的抓取表征；该光照自适应融合机制可迁移至其他多模态感知任务。
原文依据 encoding continuous illumination changes into quantitative references to guide adaptive feature fusion between RGB and non-RGB modalities
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在动态变化的光照下实现准确的衣物抓取对于服务机器人的全天候运行至关重要。然而，低光照场景中光照的减少严重降低了衣物结构特征，导致抓取鲁棒性显著下降。现有方法通常利用非RGB模态的光照不变特性来增强RGB特征，但忽略了不同光照条件下对非RGB特征依赖程度的变化，这可能在利用多模态信息时引入未对齐的非RGB线索，从而削弱模型对光照变化的适应性。为了解决这个问题，我们提出了GraspALL，一种光照-结构交互补偿模型。GraspALL的创新之处在于将连续的光照变化编码为定量参考，以根据不同的光照强度指导RGB与非RGB模态之间的自适应特征融合，从而生成光照一致的抓取表征。在自建的衣物抓取数据集上的实验表明，GraspALL在不同光照条件下相比基线将抓取准确率提高了32-44%。代码可在https://github.com/Zhonghaifeng6/GraspALL获取。
英文原文摘要 1280 字符
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