软手通过光谱与曲率传感，无电识别物体材质。
SCANS: A Soft Gripper with Curvature and Spectroscopy Sensors for In-Hand Material Differentiation
- 用流体驱动软手，集成光谱与曲率传感器，无需电子元件。
- 近红外波段敏感度高，可区分视觉相似的金属、塑料、纸张等。
- 支持触摸前与握持中识别，适合材料分类场景应用。
我们提出软曲率与光谱（SCANS）系统：一种多功能、无电子元件、流体驱动的软体操作器，可实现物体握持中或触碰前的光谱特性感知。该平台相比以往软机器人具备更广的光谱感知能力。通过材料分析，探索了适用于光谱传感的最佳软基材，并评估了触碰前与握持中的性能表现。实验表明，不同类别的物体（金属、木材、塑料、有机物、纸张、泡沫）在光谱角度差异显著，可通过线性判别分析实现可解释的统计分离。研究证实近红外波段的敏感性对区分外观相似物体至关重要。这些能力推动光学作为软机器人多模态感知的新可能。完整零部件清单、组装指南及处理代码详见：https://parses-lab.github.io/scans/。
原文摘要 · Abstract (English)
We introduce the soft curvature and spectroscopy (SCANS) system: a versatile, electronics-free, fluidically actuated soft manipulator capable of assessing the spectral properties of objects either in hand or through pre-touch caging. This platform offers a wider spectral sensing capability than previous soft robotic counterparts. We perform a material analysis to explore optimal soft substrates for spectral sensing, and evaluate both pre-touch and in-hand performance. Experiments demonstrate explainable, statistical separation across diverse object classes and sizes (metal, wood, plastic, organic, paper, foam), with large spectral angle differences between items. Through linear discriminant analysis, we show that sensitivity in the near-infrared wavelengths is critical to distinguishing visually similar objects. These capabilities advance the potential of optics as a multi-functional sensory modality for soft robots. The complete parts list, assembly guidelines, and processing code for the SCANS gripper are accessible at: https://parses-lab.github.io/scans/.
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