@incollection{escidoc:2316385,
  title = {Distributed Online Learning of Central Pattern Generators in Modular Robots},
  booktitle = {From Animals to Animats 11},
  abstract = {In this paper we study distributed online learning of locomotion gaits for modular robots. The learning is based on a stochastic ap- proximation method, SPSA, which optimizes the parameters of coupled oscillators used to generate periodic actuation patterns. The strategy is implemented in a distributed fashion, based on a globally shared reward signal, but otherwise utilizing local communication only. In a physics-based simulation of modular Roombots robots we experiment with online learn- ing of gaits and study the effects of: module failures, different robot morphologies, and rough terrains. The experiments demonstrate fast online learning, typically 5-30 min. for convergence to high performing gaits (≈ 30 cm/sec), despite high numbers of open parameters (45-54). We conclude that the proposed approach is efficient, effective and a promising candidate for online learning on many other robotic platforms.},
  volume = {6226},
  pages = {402--412},
  series = {{Lecture Notes in Computer Science}},
  publisher = {Springer},
  address = {Berlin},
  year = {2010},
  note = {author: Doncieux, Stéphan},
  slug = {escidoc-2316385},
  author = {Christensen, David Johan and Spr{\"o}witz, Alexander and Ijspeert, Auke Jan}
}