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

  title={Learning local trajectories for high precision robotic tasks: Application to KUKA LBR iiwa Cartesian positioning},
  author={Joris Gu{\'e}rin and Olivier Gibaru and Eric Nyiri and St{\'e}phane Thiery},
  journal={IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society},
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… 

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A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems

  • E. TodorovWeiwei Li
  • Mathematics
    Proceedings of the 2005, American Control Conference, 2005.
  • 2005
We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been