Learning Robot Tactile Sensing of Object for Shape Recognition Using Multi-Fingered Robot Hands

Robots can deal with different kinds of challenges using tactile sensing arrays as a primary resource. This paper demonstrates the ability of robotic hands to recognize objects' shapes using only a flexible tactile sensor arrays attached to the robotic hand's surface without building the 3D models of objects. A telemanipulation module was developed to achieve a co-moving mechanism between the robotic hand and human hands so that the robotic hand can directly learn the best way to grasp objects from human hands without additional path planning process. Tactile array data were collected while the robotic hand was performing a reiterative grasping process. By extracting the proper features from the tactile sensor array data, the support vector machines (SVMs) were employed to perform object classification. From experiments, the proposed method can achieve 96.67% classification accuracy based on sensory data and SVMs.