Generation and Optimization of Motor Behaviors in Real and Simulated Robots

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

@PhdThesis{Wolff:thesis,
  author =       "Krister Wolff",
  title =        "Generation and Optimization of Motor Behaviors in Real
                 and Simulated Robots",
  school =       "Department of Applied Mechanics, Chalmers University
                 of Technology",
  year =         "2006",
  type =         "Doctor of Philosophy",
  address =      "Goteborg, Sweden",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, autonomous
                 robots, bipedal robots, evolutionary robotics,
                 behaviour selection, behavior-based robotics, linear
                 genetic programming",
  URL =          "http://www.me.chalmers.se/~mwahde/AdaptiveSystems/PhDTheses/KristerWolff_PhDThesis.pdf",
  size =         "182 pages",
  ISBN =         "91-7291-867-5",
  abstract =     "In this thesis, the problems of generating and
                 optimising motor behaviours for both simulated and
                 real, physical robots have been investigated, using the
                 paradigms of evolutionary robotics and behaviour-based
                 robotics.

                 Specifically, three main topics have been considered:
                 (1) On-line evolutionary optimisation of hand-coded
                 gaits for real, physical bipedal robots. The evolved
                 gaits significantly outperformed the hand-coded gaits,
                 reaching up to 65percent higher speed. (2) Evolution of
                 bipedal gait controllers in simulators. First, linear
                 genetic programming was used with two different
                 simulated bipedal robots. In both these cases, the gait
                 controller was evolved starting from programs
                 consisting of random sequences of basic instructions.
                 The best evolved programs generated stable bipedal
                 locomotion, keeping the robot upright and moving
                 indefinitely. However, the evolved gaits were not very
                 human-like. Thus, a different approach, inspired by the
                 neural mechanisms involved in the locomotion of
                 biological organisms, was tried. Here, both the
                 structure and parameters of a central pattern generator
                 network, controlling the locomotion of a simulated
                 robot, were optimised using a genetic algorithm. The
                 evolved controllers generated a stable human-like gait
                 and were also able to handle gait transitions. (3)
                 Behavior selection in autonomous robots, using the
                 utility function method. In particular, the performance
                 of the method as a function of the polynomial degree of
                 the utility functions was investigated. It was found
                 that adequate behaviour selection systems can be found
                 rapidly for low polynomial degrees (1-2), but also that
                 the best solutions can only be obtained by using a
                 higher polynomial degree (3-4). Furthermore, the
                 performance of different evolutionary algorithms in
                 connection with the utility function method was also
                 investigated and, somewhat surprisingly, it was found
                 that the standard method, employing a simple genetic
                 algorithm, generally outperformed the modified
                 methods.",
  notes =        "Printed by Chalmers Reproservice Goteborg, Sweden
                 2006",
}

Genetic Programming entries for Krister Wolff

Citations