SCANS: A Soft Gripper with Curvature and Spectroscopy Sensors for In-Hand Material Differentiation
Nathaniel Hanson, Austin Allison, Charles A DiMarzio, Taskin Padir, Kristen Dorsey
AI summary
Problem
Soft robotic grippers currently lack the ability to distinguish subtle material properties and cannot perceive object characteristics before contact, limiting adaptive manipulation. Existing spectral sensors are often rigid, generate heat, or suffer from poor optical throughput in deformable environments.
Approach
The authors developed SCANS, a soft gripper that embeds PMMA optical waveguides and prisms to simultaneously measure finger curvature and capture reflected near-infrared spectra without embedding electronics in the actuated region.
Key results
- Infrared spectral characterization of common soft robotic materials to guide optical design.
- Dual-use optical architecture enabling real-time curvature estimation and high-fidelity in-hand spectral sensing.
- Statistically separable spectral signatures across diverse material classes (metal, wood, plastic, organic, paper, foam).
- Near-infrared wavelengths identified as critical for distinguishing visually similar objects.
Why it matters
This platform advances soft robotics by providing a robust, electronics-free sensory modality for pre-touch and in-hand material classification, enabling more adaptive and safer manipulation in unstructured environments.
Abstract
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/.