@inproceedings{NguyenTuongP2011_3,
  title = {Learning task-space tracking control with kernels },
  abstract = {Task-space tracking control is essential for robot manipulation. In practice, task-space control of redundant robot systems is known to be susceptive to modeling errors. Here, data driven 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 non-convex 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 taskspace tracking control. For evaluations, we show in simulation the ability of the method for online model learning for task-space tracking control of redundant robots.},
  pages = {704-709 },
  editors = {Amato, N.M.},
  publisher = {IEEE},
  address = {Piscataway, NJ, USA},
  month = sep,
  year = {2011},
  slug = {nguyentuongp2011_3},
  author = {Nguyen-Tuong, D. and Peters, J.},
  month_numeric = {9}
}