What can be inferred from a tactile arrayed sensor in autonomous in-hand manipulation?

In this paper, we present a concept of using tactile sensors as sufficient tools in localizing, recognizing an object in robotic in-hand manipulation tasks. Our approach operates on a moderately intensive array data that are obtained from a tactile sensor when a robotic gripper grasps an object that is small relatively to fingers. In stead of using tactile data as an array of discrete numbers, we treat it as a grayscale image. By working with successive images from tactile sensor exploiting image processing tools, we are able to extract rich information about the contact condition between an object and the gripper. Experimental results show that from the processed data, we can realize the grasped object's position/orientation, contact shape, especially the stick-slip condition on the contact surface that is derived for the first time for this sensor. Based on localized displacement phenomenon of a sliding soft object, we proposed an approach to detect the slippage quantitatively, and conducted a wide range of experiment in term of characteristics of object's movement. We also conducted a model for an object-grasping gripper with tactile feedback in various postures of the object, and a corresponding experiment setup to validate computed results. Presented result is expected to be used widely in the community due to its simplicity and reliability.