| Volume 27 Issue 3-4 - Publication Date: 1 March 2008 |
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| Learning to Move in
Modular Robots using
Central Pattern
Generators and Online
Optimization |
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| Alexander Sproewitz, Rico Moeckel, Jérôme Maye and Auke Jan Ijspeert
School of Computer and Communication Science
Ecole Polytechnique Fédérale de Lausanne
Station 14, CH-1015 Lausanne, Switzerland |
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| This article addresses the problem of how modular robotics systems,
i.e. systems composed of multiple modules that can be configured
into different robotic structures, can learn to locomote. In particular,
we tackle the problems of online learning, that is, learning while
moving, and the problem of dealing with unknown arbitrary robotic
structures. We propose a framework for learning locomotion controllers
based on two components: a central pattern generator (CPG)
and a gradient-free optimization algorithm referred to as Powell’s
method. The CPG is implemented as a system of coupled nonlinear
oscillators in our YaMoR modular robotic system, with one oscillator
per module. The nonlinear oscillators are coupled together across
modules using Bluetooth communication to obtain specific gaits, i.e.
synchronized patterns of oscillations among modules. Online learning
involves running the Powell optimization algorithm in parallel
with the CPG model, with the speed of locomotion being the criterion
to be optimized. Interesting aspects of the optimization include the
fact that it is carried out online, the robots do not require stopping
or resetting and it is fast. We present results showing the interesting
properties of this framework for a modular robotic system. In particular,
our CPG model can readily be implemented in a distributed
system, it is computationally cheap, it exhibits limit cycle behavior
(temporary perturbations are rapidly forgotten), it produces smooth
trajectories even when control parameters are abruptly changed and
it is robust against imperfect communication among modules. We
also present results of learning to move with three different robot
structures. Interesting locomotion modes are obtained after running
the optimization for less than 60 minutes. |
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Description |
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Example
1: Snake configuration, random gait (2.3 MB) avi |
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Example
2: Snake configuration, efficient gait (2.5 MB) avi |
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Example
3: Tripod configuration, random gait (1.3 MB) avi |
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Example
4: Tripod configuration, efficient gait (1.8 MB) avi |
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Example
5: Quadruped configuration, random gait (1.8 MB) avi |
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6 |
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Example
6: Quadruped configuration, efficient gait (1.8 MB) avi |
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