Morphology Independent Learning in Modular Robots

David Johan Christensen, Mirko Bordignon, Ulrik Pagh Schultz, Danish Shaikh, Kasper Støy

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Abstrakt

Hand-coding locomotion controllers for modular robots is difficult due to their polymorphic nature. Instead, we propose to use a simple and distributed reinforcement learning strategy. ATRON modules with identical controllers can be assembled in any configuration. To optimize the robot’s locomotion speed its modules independently and in parallel adjust their behavior based on a single global reward signal. In simulation, we study the learning strategy’s performance on different robot configurations. On the physical platform, we perform learning experiments with ATRON robots learning to move as fast as possible. We conclude that the learning strategy is effective and may be a practical approach to design gaits.


OriginalsprogEngelsk
TitelProceedings of the International Symposium on Distributed Autonomous Robotic Systems
RedaktørerH. Asama, H. Kurokawa, J. Ota, K. Sekiyama
Antal sider12
ForlagSpringer
Publikationsdato2009
Sider379-391
ISBN (Trykt)978-3-642-00643-2
StatusUdgivet - 2009
BegivenhedDistributed Autonomous Robotic Systems - Tsukuba, Japan
Varighed: 17. nov. 200819. nov. 2008

Konference

KonferenceDistributed Autonomous Robotic Systems
Land/OmrådeJapan
ByTsukuba
Periode17/11/200819/11/2008

Emneord

  • Selvrekonfigurerbare Robotter
  • Robot
  • Modulære Robotter
  • Robot Learning

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