Publications Freek Stulp

@InProceedings{stulp10reinforcement,
  title                    = {Reinforcement Learning of Full-body Humanoid Motor Skills},
  author                   = {Freek Stulp and Jonas Buchli and Evangelos Theodorou and Stefan Schaal},
  booktitle                = {10th IEEE-RAS International Conference on Humanoid Robots},
  year                     = {2010},
  note                     = {{\bf Best paper finalist}},
  pages                    = {405-410},
  abstract                 = {Applying reinforcement learning to humanoid robots is challenging because humanoids have a large number of degrees of freedom and state and action spaces are continuous. Thus, most reinforcement learning algorithms would become computationally infeasible and require a prohibitive amount of trials to explore such high-dimensional spaces. In this paper, we present a probabilistic reinforcement learning approach, which is derived from the framework of stochastic optimal control and path integrals. The algorithm, called Policy Improvement with Path Integrals (PI^2), has a surprisingly simple form, has no open tuning parameters besides the exploration noise, is model-free, and performs numerically robustly in high dimensional learning problems. We demonstrate how PI^2 is able to learn full-body motor skills on a 34-DOF humanoid robot. To demonstrate the generality of our approach, we also apply PI^2 in the context of variable impedance control, where both planned trajectories and gain schedules for each joint are optimized simultaneously.},
  bib2html_pubtype         = {Refereed Conference Paper, Awards},
  bib2html_rescat          = {Reinforcement Learning of Robot Skills}
}