Learning local trajectories for high precision robotic tasks: Application to KUKA LBR iiwa Cartesian positioning

@article{Gurin2016LearningLT,
  title={Learning local trajectories for high precision robotic tasks: Application to KUKA LBR iiwa Cartesian positioning},
  author={J. Gu{\'e}rin and O. Gibaru and E. Nyiri and St{\'e}phane Thiery},
  journal={IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society},
  year={2016},
  pages={5316-5321}
}
To ease the development of robot learning in industry, two conditions need to be fulfilled. Manipulators must be able to learn high accuracy and precision tasks while being safe for workers in the factory. In this paper, we extend previously submitted work which consist in rapid learning of local high accuracy behaviors. By exploration and regression, linear and quadratic models are learnt for respectively the dynamics and cost function. Iterative Linear Quadratic Gaussian Regulator combined… Expand
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