Distributed Online Learning of Central Pattern Generators in Modular Robots

2010

Book Chapter

dlg

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.

@incollection{escidoc:2316385,
  title = {Distributed Online Learning of Central Pattern Generators in Modular Robots},
  author = {Christensen, David Johan and Spr{\"o}witz, Alexander and Ijspeert, Auke Jan},
  booktitle = {From Animals to Animats 11},
  volume = {6226},
  pages = {402--412},
  series = {{Lecture Notes in Computer Science}},
  publisher = {Springer},
  address = {Berlin},
  year = {2010},
  note = {author: Doncieux, Stéphan},
  doi = {10.1007/978-3-642-15193-4\textunderscore38}
}