BiGSiD: Bionic Grasping with Edge-AI Slip Detection

Object grasping is a crucial task for robots, inspired by nature, where humans can flexibly grasp any object and detect whether it is slipping from grasp or not, more by the sense of touch than vision. In this work we present a bionic gripper with an Edge-AI device that is able to dexterously grasp the handled objects, sense and predict their slippage. In this paper, a bionic gripper with tactile sensors and a time-of-flight sensor is developed. We propose a LSTM model which is used to detect (incipient) slip/slippage, where a 6 degree-of-freedom robot manipulator is used for data collection and testing. The aim of this paper is to develop an efficient slip detection system which we can deploy on the edge device on our gripper, so it can be a stand-alone product that can be attached to almost any robotic manipulator. We have collected a dataset, trained the model and achieved a slip detection accuracy of 95.34%. Due to the efficiency of our model we were able to implement the slip detection on an edge device. We use the Nvidia Jetson AGX Orin development board to show the inference/prediction in a real-time scenario. We demonstrate in the our experiments how the on-gripper slip detection capability allows more robust grasping as the grip force is adjusted in response to a slippage.