Evolutionary Optimization of a Bipedal Gait in a Physical Robot

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Wolff:2008:cec,
  author =       "Krister Wolff and David Sandberg and Mattias Wahde",
  title =        "Evolutionary Optimization of a Bipedal Gait in a
                 Physical Robot",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "440--445",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0123.pdf",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04630835",
  DOI =          "doi:10.1109/CEC.2008.4630835",
  size =         "6 pages",
  abstract =     "Evolutionary Optimization of a gait for a bipedal
                 robot has been studied, combining structural and
                 parametric modifications of the system responsible for
                 generating the gait. The experiment was conducted using
                 a small 17 DOF humanoid robot, whose actuators consist
                 of 17 servo motors. In the approach presented here,
                 individuals representing a gait consisted of a sequence
                 of set angles (referred to as states) for the servo
                 motors, as well as ramping times for the transition
                 between states. A hand-coded gait was used as starting
                 point for the Optimization procedure: A population of
                 30 individuals was formed, using the hand-coded gait as
                 a seed. An evolutionary procedure was executed for 30
                 generations, evaluating individuals on the physical
                 robot. New individuals were generated using mutation
                 only. There were two different mutation operators,
                 namely (1) parametric mutations modifying the actual
                 values of a given state, and (2) structural mutations
                 inserting a new state between two consecutive states in
                 an individual. The best evolved individual showed an
                 improvement in walking speed of approximately
                 65percent.",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",
}

Genetic Programming entries for Krister Wolff David Sandberg Mattias Wahde

Citations