Research on Shape Perception of the Soft Gripper Based on Triboelectric Nanogenerator Soft robotics is an exciting novel research field and has great potentiality in humanmachine cooperation. This kind of robot can undergo large deformations to execute complex motions, which leads to the difficulty to apply traditional sensors and the lack of necessary feedback. Therefore, we want to create a sensory finger that is able to grasp and hold heavy objects. This work explores the potential of triboelectric nanogenerators (TENGs) and presents the demonstration of TENG based on tactile sensors integrated into the structure of a soft finger with variable stiffness. For displaying the sensing result, three soft fingers were assembled to form a soft gripper. Our result shows that the tactile sensor with strip electrodes can detect the contact position when comparing the output difference in different electrodes. In order to describe the shape of the grab target, in this paper, we theoretically analyze the relative position relationship of the end before and after the finger bending deformation, and obtain the fingerbending mode. In the demonstration, the gripper integrating with the sensor can roughly distinguish the scale of the object by combining tactile perception capability. The proposed highly pliable fingers can help the robot handle the fragile and soft objects and appropriately recognize their shapes in complex environment.

Predicting Grasp Success with a Soft Sensing Skin and ShapeMemory Actuated Gripper Tactile sensors have been increasingly used to support rigid robot grippers in object grasping and manipulation. However, rigid grippers are often limited in their ability to handle compliant, delicate, or irregularly shaped objects. In recent years, grippers made from soft and flexible materials have become increasingly popular for certain manipulation tasks, e.g., grasping, due to their ability to conform to the object shape without the need for precise control. Although promising, such soft robot grippers currently suffer from the lack of available sensing modalities. In this work, we introduce a soft and stretchable sensing skin and incorporate it into the two fingers of a shapememory actuated soft gripper. The onboard sensing skin includes a 9axis inertial measurement unit (IMU) and five discrete pressure sensors per finger. We use this sensorized soft gripper to study grasp success and stability of over 2585 grasps with various objects using several machine learning methods. Our experiments show that LSTMs were the most accurate predictors of grasp success and stability, compared to SVMs, FFNNs, and STHMP. We also evaluated the effects on performance of each sensoru0027s data, and the success rates for individual objects. The results show that the accelerometer data of the IMUs has the largest contribution to the overall grasp prediction, which we attribute to its ability to detect precise movements of the gripper during grasping.