Plant-Inspired Poroelastic Skins for Low-Cost Tactile Sensing in Soft Robotics

Soft robots require tactile sensors that can quantify the mechanical properties of unknown objects, yet most existing technologies are fragile, costly, or limited in range. We present a plant-inspired poroelastic skin that converts contact deformation into nonlinear hydraulic signals via liquid-filled channels in an elastomeric matrix. By tuning skin stiffness and channel geometry, sensitivity can be matched to target modulus ranges, enabling accurate measurements over more than two orders of magnitude. Integrated into a low-cost (under US$50) 3D-printed robotic arm, the skin combines real-time measurements of object diameter, deformation, and internal pressure with an analytical inverse model to estimate effective Young's modulus without direct force sensing. A universal operational window, defined by the ratio of object to skin stiffness, maximizes accuracy independent of absolute modulus. Validation on polymers and fruits demonstrates applications from laboratory material testing to non-destructive monitoring of ripening, advancing accessible, quantitative tactile sensing in soft robotics.