Slip Detection for Grasp Stabilization With a Multifingered Tactile Robot Hand
Jasper W. James, Nathan F. Lepora
Abstract
Tactile sensing is used by humans when grasping to prevent us dropping objects. One key facet of tactile sensing is slip detection, which allows a gripper to know when a grasp is failing and take action to prevent an object being dropped. This study demonstrates the slip detection capabilities of the recently developed Tactile Model O (T-MO) robotic hand by using support vector machines to detect slip and test multiple slip scenarios including responding to the onset of slip in real time with 11 different objects in various grasps. In this article, we demonstrate the benefits of slip detection in grasping by testing two real-world scenarios: adding weight to destabilize a grasp and using slip detection to lift up objects at the first attempt. The T-MO is able to detect when an object is slipping, react to stabilize the grasp, and be deployed in real-world scenarios. This shows the T-MO is a suitable platform for autonomous grasping by using reliable slip detection to ensure a stable grasp in unstructured environments.