Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger

Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment. In this paper, we present an embedded sensing solution using optical fibers for an omni-adaptive soft robotic finger with exceptional adaptation in all directions. In particular, we managed to insert a pair of optical fibers inside the finger's structural cavity without interfering with its adaptive performance. The resultant integration is scalable as a versatile, low-cost, and moisture-proof solution for physically safe human-robot interaction. In addition, we experimented with our finger design for an object sorting task and identified sectional diameters of 94\% objects within the $\pm$6mm error and measured 80\% of the structural strains within $\pm$0.1mm/mm error. The proposed sensor design opens many doors in future applications of soft robotics for scalable and adaptive physical interactions in the unstructured environment.Comment: 8 pages, 6 figures, full-length version of a submission to IEEE RoboSoft 202

Automated Recycling Separation Enabled by Soft Robotic Material Classification

Single-stream recycling is currently an extremely labor intensive process due to the need for manual object sorting. Soft robotics offers a natural solution as compliant robots require less computation to plan paths and grasp objects in a cluttered environment. However, most soft robots are not robust enough to handle the many sharp objects present in a recycling facility. In this work, we present a soft sensorized robotic gripper which is fully electrically driven and can detect the difference between paper, metal and plastic. By combining handed shearing auxetics with high deformation capacitive pressure and strain sensors, we present a new puncture resistant soft robotic gripper. Our materials classifier has 85% accuracy with a stationary gripper and 63% accuracy in a simulated recycling pipeline. This classifier works over a variety of objects, including those that would fool a purely vision-based system.National Science Foundation (Grant 1830901