Adaptive critic-based neural network object contact controller for a three-finger gripper

MAR'S greenhouse operation requires robot arms that are capable of manipulating objects such as plant trays, fruits, vegetables and so on. Grasping and manipulation of objects have been a challenging task for robots. It is important that the manipulator performs these tasks accurately and faster with out damaging the object. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact subtask is defined in terms of following a trajectory accurately so that the object to be grasped is in contact with the gripper. The proposed controller scheme consists of a feedforward action generating neural network (NN) that compensates for the nonlinear gripper and object contact dynamics. The learning of this NN is performed online based on a critic signal so that a 3-finger gripper tracks a predefined desired trajectory, which is specified in terms of a desired position and velocity for object contact control. Novel weight tuning updates are derived for the action generating NN and a Lyapunov-based stability analysis is presented. Simulation results are shown for a 3-finger gripper making contact with an object.