Plant-Inspired Elastic-Hydraulic Tactile Sensing Enables Quantitative Stiffness Estimation in Soft Robots.

Soft robots require tactile sensors capable of quantifying the mechanical properties of unknown objects during manipulation, yet most existing approaches are fragile, costly, or limited in dynamic range. Here, we present a model-guided plant-inspired hydraulic tactile sensor in which contact-induced deformation of a compliant elastomer generates a measurable pressure change in an embedded liquid-filled channel. By combining pressure and deformation measurements with an analytical elastic-hydraulic contact model, the effective Young's modulus of the contacted spherical object can be inferred without direct force sensing. The accessible stiffness range is set by the sensor's elastic modulus and channel geometry; we demonstrate this design tunability using four sensor variants, enabling accurate stiffness estimation over more than two orders of magnitude. Integrated into a low-cost (under US$50) three-dimensional-printed robotic arm, the sensor performs real-time modulus estimation under quasi-static conditions using measurements of object diameter, deformation, and internal pressure. A predictable operating window, expressed as the stiffness ratio between the object and the sensor, maximizes measurement accuracy within the model's linear-elastic regime. Validation on synthetic polymers and fresh produce demonstrates applications ranging from laboratory material characterization to nondestructive monitoring of fruit ripening, advancing accessible and quantitative tactile sensing for soft robotic systems.