A Distributed Strategy for Gait Adaptation in Modular Robots

David Johan Christensen; Ulrik Pagh Schultz; Kasper Stoy

doi:10.1109/ROBOT.2010.5509942

A Distributed Strategy for Gait Adaptation in Modular Robots

David Johan Christensen, Ulrik Pagh Schultz, Kasper Stoy

University of Southern Denmark

Research output: Contribution to journal › Conference article › Research › peer-review

Abstract

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 language	English
Journal	I E E E International Conference on Robotics and Automation. Proceedings
Pages (from-to)	2765-2770
ISSN	1050-4729
DOIs	https://doi.org/10.1109/ROBOT.2010.5509942
Publication status	Published - 2010
Externally published	Yes
Event	2010 IEEE International Conference on Robotics and Automation - Anchorage, United States Duration: 3 May 2010 → 8 May 2010 https://ieeexplore.ieee.org/xpl/conhome/5501116/proceeding

Conference

Conference	2010 IEEE International Conference on Robotics and Automation
Country/Territory	United States
City	Anchorage
Period	03/05/2010 → 08/05/2010
Internet address	https://ieeexplore.ieee.org/xpl/conhome/5501116/proceeding

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10.1109/ROBOT.2010.5509942

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title = "A Distributed Strategy for Gait Adaptation in Modular Robots",

abstract = "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{\textquoteright}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.",

author = "Christensen, {David Johan} and Schultz, {Ulrik Pagh} and Kasper Stoy",

year = "2010",

doi = "10.1109/ROBOT.2010.5509942",

language = "English",

pages = "2765--2770",

journal = "I E E E International Conference on Robotics and Automation",

issn = "2152-4092",

publisher = "IEEE",

note = "2010 IEEE International Conference on Robotics and Automation, ICRA 2010 ; Conference date: 03-05-2010 Through 08-05-2010",

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AU - Schultz, Ulrik Pagh

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AB - 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.

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DO - 10.1109/ROBOT.2010.5509942

M3 - Conference article

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JO - I E E E International Conference on Robotics and Automation

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SN - 2152-4092

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ER -

A Distributed Strategy for Gait Adaptation in Modular Robots

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