Abstract — In this paper, we present two methods for the estimation of a globally valid visual-motor model of a robotic manipulator. In conventional uncalibrated visual servoing, the visuo-motor function is approximated locally with a Jacobian. However, for optimal task planning, or nonlinear controller design with global stability guarantee, one needs to know a model that provides some information about the behavior of the system over the whole workspace. Our presented methods remedy this drawback in uncalibrated visual servoing by incrementally building a global estimator based on the movement history. We implement two such methods. The first method is a K-nearest neighborhood regressor over Jacobian that uses previously estimated local models. The second method stores previous movements and computes an estimate of the Jacobian by solving a local least squares problem. Experimental results show that both methods provide better global estimation quality compared to the conventional local estimation method with much lower estimation variance. I.

It is desirable for a robot to be able to operate in unstructured environments. In this paper, we demonstrate how a robot can learn primitive skills and we show how to augment them. We formalize 2D-decidable (pointing) and 3D-decidable (reaching) skills within an uncalibrated visual servoing framework. Skill decidability is defined in conjunction with an image-based controller, which has local asymptotic stability. In addition, we propose sequential composition of primitive skills to combine pointing and reaching skills in order to increase the accuracy of reaching skill. We use simple primitive tasks such as multi-point alignment and point-to-line alignment. We validate our results with real uncalibrated eye-in-hand experiments with a 4-DOF WAM from Barrett Technology Inc., alongside computer simulations.