High-order neural networks for the learning of robot contact surface shape

It is known that the problem of learning the shape parameters of unknown surfaces that are in contact with a robot end-effector can be formulated as a nonlinear parameter estimation problem and an extended Kalman filter can be applied in order to estimate the surface shape parameters. In this paper, we show that the problem of learning the shape parameters of unknown contact surfaces can be formulated as a linear parameter estimation problem and thus globally convergent learning laws can be applied. Moreover, we show that by using appropriate neural network approximators, the unknown surfaces can be learned even if there are no force measurements, i.e., the robot is not provided with any force or tactile sensors.