Slip detection for grasp stabilisation with a multi-fingered tactile robot hand
It addresses the problem of preventing object drops in robotics for autonomous grasping, but is incremental as it applies existing methods to a new platform.
This study tackled slip detection for grasp stabilization using the Tactile Model O (T-MO) robot hand, demonstrating its ability to detect slip with support vector machines and react in real time across eleven objects and various grasps, enabling stable autonomous grasping in unstructured environments.
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) by using support vector machines to detect slip and test multiple slip scenarios including responding to the onset of slip in real time with eleven different objects in various grasps. We demonstrate the benefits of slip detection in grasping by testing two real-world scenarios: adding weight to destabilise 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 stabilise 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. Supplementary video: https://youtu.be/wOwFHaiHuKY