Neural network learning of robot arm impedance in operational space.

Impedance control is one of the most effective control methods for the manipulators in contact with their environments. The characteristics of force and motion control, however, is determined by a desired impedance parameter of a manipulator's end-effector that should be carefully designed according to a given task and an environment. The present paper proposes a new method to regulate the impedance parameter of the end-effector through learning of neural networks. Three kinds of the feed-forward networks are prepared corresponding to position, velocity and force control loops of the end-effector before learning. First, the neural networks for position and velocity control are trained using iterative learning of the manipulator during free movements. Then, the neural network for force control is trained for contact movements. During learning of contact movements, a virtual trajectory is also modified to reduce control error. The method can regulate not only stiffness and viscosity but also inertia and virtual trajectory of the end-effector. Computer simulations show that a smooth transition from free to contact movements can be realized by regulating impedance parameters before a contact.