Robotic Grasp Stability Analysis Using Extreme Learning Machine

Recently, autonomous grasping of unknown objects is a fundamental requirement for robots performing manipulation tasks in real world environments. It is still considered as a challenging problem no matter how process we have made. It is significant that how the robot to judge the stability of grabbing object. In this paper, we analyze the data through process of grabbing 3 objects whether is successful or failed by constructing Global Alignment kernel with Extreme Learning Machine and Support Vector Machine. For comparative analysis, the Barrett hand's finger angles and robot joint angles are also recorded. By processing obtained data in different ways, we have comparative results in various modes. Experiments denote the tactile results achieve better performance than the finger angle's and robot joint angle's.