Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives

@article{Silvrio2019UncertaintyAwareIL,
  title={Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives},
  author={Jo{\~a}o Silv{\'e}rio and Yanlong Huang and Fares J. Abu-Dakka and L. Rozo and D. Caldwell},
  journal={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2019},
  pages={90-97}
}
During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new… Expand
A Linearly Constrained Nonparametric Framework for Imitation Learning
  • Yanlong Huang, D. Caldwell
  • Computer Science, Mathematics
  • 2020 IEEE International Conference on Robotics and Automation (ICRA)
  • 2020
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