Jacobian Learning Methods for Tasks Sequencing in Visual Servoing

In this paper, the coupling between Jacobian learning and task sequencing through the redundancy approach is studied. It is well known that visual servoing is robust to modeling errors in the Jacobian matrices. This justifies why Jacobian estimation does not usually degrade the system convergence. However, we show that this is not true any more when the redundancy formalism is used. In this case the Jacobian matrix is also necessary to compute projection operators for task decomposition, which is quite sensitive to errors. We show that learning improves the servoing performance, when task sequencing is used. Conversely, sequencing improves the convergence of learning, especially for tasks involving several degrees of freedom. Eye-in-hand and eye-to-hand experiments have been performed on two robots with six degrees of freedom