CARTE seed projects seminar welcomes Zhanfeng Zhou (PhD candidate in Microfluidics and BioMEMS Laboratory, working under the supervision of Professor Xinyu Liu)

To attend this seminar, please contact CARTE[AT]utoronto.ca

Abstract: Soft robotic grippers possess high structural compliance and adaptability for grasping objects with unknown and irregular shapes and sizes. To enable more dexterous manipulation, soft sensors with similar mechanical properties to common elastomer materials are desired to be integrated into soft grippers. In this project, we develop ionic hydrogel-based strain and tactile sensors and integrate these sensors into a three-finger soft gripper for learning-based object recognition and force-controlled grasping. Such hydrogel-based sensors have excellent conductivity, high stretchability and toughness, good ambient stability, and unique anti-freezing property; they can be readily attached to a soft gripper at desired locations for strain and tactile sensing. Based on a deep-learning model, we demonstrate the capability of the sensory soft gripper for object grasping and recognition at both room and freezing temperatures and achieve high recognition accuracy close to 100\% for 10 typical objects. Moreover, the capacitive tactile feedback of the gripper is utilized to develop a closed-loop force controller and realize force-controlled grasping of fragile or highly deformable objects. A slip detection and compensation strategy are also proposed and validated for the sensory gripper for adjusting the grasping force in real time upon detecting slippage.