Latent space policy search for robotics

  title={Latent space policy search for robotics},
  author={K. Luck and G. Neumann and Erik Berger and Jan Peters and H. B. Amor},
  journal={2014 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  • K. Luck, G. Neumann, +2 authors H. B. Amor
  • Published 2014
  • Computer Science
  • 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
Learning motor skills for robots is a hard task. In particular, a high number of degrees-of-freedom in the robot can pose serious challenges to existing reinforcement learning methods, since it leads to a high-dimensional search space. However, complex robots are often intrinsically redundant systems and, therefore, can be controlled using a latent manifold of much smaller dimensionality. In this paper, we present a novel policy search method that performs efficient reinforcement learning by… Expand
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  • K. Luck, H. B. Amor
  • Computer Science
  • 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2017


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