Online Kernel-Based Learning for Task-Space Tracking Robot Control

Task-space control of redundant robot systems based on analytical models is known to be susceptive to modeling errors. Data-driven model learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an ill-posed problem. In particular, the same input data point can yield many different output values, which can form a nonconvex solution space. Because the problem is ill-posed, models cannot be learned from such data using common regression methods. While learning of task-space control mappings is globally ill-posed, it has been shown in recent work that it is locally a well-defined problem. In this paper, we use this insight to formulate a local kernel-based learning approach for online model learning for task-space tracking control. We propose a parametrization for the local model, which makes an application in task-space tracking control of redundant robots possible. The model parametrization further allows us to apply the kernel-trick and, therefore, enables a formulation within the kernel learning framework. In our evaluations, we show the ability of the method for online model learning for task-space tracking control of redundant robots.