A Distributed Strategy for Gait Adaptation in Modular Robots

David Johan Christensen, Ulrik Pagh Schultz, Kasper Stoy

Research output: Contribution to journalConference articleResearchpeer-review


In this paper we study online gait opti- mization for modular robots. The learning strategy we apply is distributed, independent on robot mor- phology, and easy to implement. First we demonstrate how the strategy allows an ATRON robot to adapt to faults and changes in its morphology and we study the strategy’s scalability. Second we extend the strategy to learn the parameters of gait-tables for ATRON and M-TRAN robots.We conclude that the presented strategy is effective for online learning of gaits for most types of modular robots and that learning can effectively be distributed by having independent pro- cesses learning in parallel.
Original languageEnglish
JournalI E E E International Conference on Robotics and Automation. Proceedings
Pages (from-to)2765-2770
Publication statusPublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Robotics and Automation - Anchorage, AK, United States
Duration: 3 May 20108 May 2010


Conference2010 IEEE International Conference on Robotics and Automation
CountryUnited States
CityAnchorage, AK
Internet address

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