Incremental local online Gaussian Mixture Regression for imitation learning of multiple tasks

@article{Cederborg2010IncrementalLO,
  title={Incremental local online Gaussian Mixture Regression for imitation learning of multiple tasks},
  author={Thomas Cederborg and Ming Li and Adrien Baranes and Pierre-Yves Oudeyer},
  journal={2010 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2010},
  pages={267-274}
}
Gaussian Mixture Regression has been shown to be a powerful and easy-to-tune regression technique for imitation learning of constrained motor tasks in robots. Yet, current formulations are not suited when one wants a robot to learn incrementally and online a variety of new context-dependant tasks whose number and complexity is not known at programming time, and when the demonstrator is not allowed to tell the system when he introduces a new task (but rather the system should infer this from the… CONTINUE READING
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