Learning-Based Object Recognition Via a Eutectogel Electronic Skin Enabled Soft Robotic Gripper

Compared to the traditional robot, which is rigidly structured, the soft robot, usually made of soft material, or fol- lowing a continuous movement pattern, has attracted extensive attention due to its unique features, such as high adaptivity to various unstructured environments and safe interaction with living beings through the deformable interface. However, mechanical and morphological requirements limit the design and implementation of a compatible sensing module, which restricts the further devel- opment of robotic functionality. Here, we designed a flexible soft sensingWirewiththepiezoresistiveEutectogelpackedinanEcoflex tube (WEE), which is sensitive, stable, and easily manipulated. The wire and its array facilitated the perception function of the soft gripper and acted as the Electronic skin (E-skin) to acquire information from grasped objects. With the built-in E-skin, the gripper achieved object recognition at an accuracy of 93.78% for standard geometric objects in 9 categories based on a machine learning model. In addition, our design successfully demonstrated its application in fruit sorting, which proves its robustness and versatility. The proposed WEE-based E-skin can be easily applied to other soft robots with facile integration and further expedites advanced functionalization in robot-object interaction. IndexTerms—Softrobotapplications,softsensorsandactuators, modeling, control, learning for soft robots, E-skin, soft gripper.