An under-actuated manipulation controller based on Workspace Analysis and Gaussian Processes

The kinematic modelling has been applied to many controllers of under-actuated manipulators. Most of these studies assume that the control process is conducted within the workspace. However, as such a kinematic model cannot describe the situations when the stable grasping is violated in the real environment, these controllers may fail unexpectedly. In this paper, we propose a combination of kinematics based Workspace Analysis (WA) and Gaussian Process Classification (GPC) to model the success rates of control actions in the theoretical workspace. We also use the Gaussian Process Regression (GPR) to model the residual between the prediction of the WA and the ground truth data. We then apply this integrated model, Gaussian Processes enhanced Workspace Analysis (GP-WA), into an optimal controller. The optimal controller is implemented on a planar under-actuated gripper with two three-phalanx fingers. Two sets of simulation experiments are carried out to validate our method. The results demonstrate that the optimal manipulation controller based on GP-WA achieves high control accuracy for manipulating a wide range of objects.