Publication: Research - peer-review › Article in proceedings – Annual report year: 2010
Without internal affiliation
In this paper we study distributed online learning of locomotion gaits for modular robots. The learning is based on a stochastic approximation 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 learning 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.
|Title of host publication||From Animals to Animats 11|
|Editors||R. Goebel, J. Siekmann, W. Wahlster|
|Conference||11th International Conference on Simulation of Adaptive Behavior|
|Period||25/08/10 → 28/08/10|
|Name||Lecture Notes in Computer Science|
|Citations||Web of Science® Times Cited: No match on DOI|
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