@inproceedings{Schaal_PICIRS_1994,
  title = {Robot learning by nonparametric regression},
  booktitle = {Proceedings of the International Conference on Intelligent Robots and Systems (IROS'94)},
  abstract = {We present an approach to robot learning grounded on a nonparametric regression technique, locally weighted regression. The model of the task to be performed is represented by infinitely many local linear models, i.e., the (hyper-) tangent planes at every query point. Such a model, however, is only generated when a query is performed and is not retained. This is in contrast to other methods using a finite set of linear models to accomplish a piecewise linear model. Architectural parameters of our approach, such as distance metrics, are also a function of the current query point instead of being global. Statistical tests are presented for when a local model is good enough such that it can be reliably used to build a local controller. These statistical measures also direct the exploration of the robot. We explicitly deal with the case where prediction accuracy requirements exist during exploration: By gradually shifting a center of exploration and controlling the speed of the shift with local prediction accuracy, a goal-directed exploration of state space takes place along the fringes of the current data support until the task goal is achieved. We illustrate this approach by describing how it has been used to enable a robot to learn a challenging juggling task: Within 40 to 100 trials the robot accomplished the task goal starting out with no initial experiences.},
  pages = {478-485},
  publisher = {Munich Germany},
  year = {1994},
  note = {clmc},
  slug = {schaal_picirs_1994},
  author = {Schaal, S. and Atkeson, C. G.},
  crossref = {p867}
}