A Practical Platform for On-Line Genetic Programming for Robotics

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Terence Soule and Robert B. Heckendorn",
  title =        "A Practical Platform for On-Line Genetic Programming
                 for Robotics",
  booktitle =    "Genetic Programming Theory and Practice X",
  year =         "2012",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Marylyn D. Ritchie and Jason H. Moore",
  publisher =    "Springer",
  chapter =      "2",
  pages =        "15--29",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  keywords =     "genetic algorithms, genetic programming, Cotsbots,
                 On-line, On-board, Robots, Swarms",
  isbn13 =       "978-1-4614-6845-5",
  URL =          "http://dx.doi.org/10.1007/978-1-4614-6846-2_2",
  DOI =          "doi:10.1007/978-1-4614-6846-2_2",
  abstract =     "There is growing interest in on-line evolution for
                 autonomous robots. On-line learning is critical to
                 achieve high levels of autonomy in the face of dynamic
                 environments, tasks, and other variable elements
                 encountered in real world environments. Although a
                 number of successes have been achieved with on-line
                 evolution, these successes are largely limited to
                 fairly simple learning paradigms, e.g. training small
                 neural networks of relatively few weights and in
                 simulated environments. The shortage of more complex
                 learning paradigms is largely due to the limitations of
                 affordable robotic platforms, which tend to be woefully
                 underpowered for such applications.

                 In this paper we introduce a simple robotics platform
                 based on Commodity Off The Shelf (COTS) design
                 principles that makes on-line genetic programming for
                 robotics practical and affordable. We compare the
                 relative strengths and weaknesses of a number of
                 different build options. As a proof-of-concept we
                 compare three variations of evolutionary learning
                 models for a colour-following problem on a robot based
                 on one of the designs: a simple neural network learning
                 framework of the type typically seen in current
                 research, a more extensive learning model that could
                 not be supported by traditional low-cost research
                 robots, and a simple evolutionary algorithm, but using
                 standard tree-based genetic programming representation,
                 which is also beyond the scope of traditional low-cost
                 research robots. Our results show that the more
                 powerful evolutionary models enabled by more powerful
                 robots significantly improves the on-line evolutionary
                 performance and thus that there are practical benefits
                 to the COTS based",
  notes =        "part of \cite{Riolo:2012:GPTP} published after the
                 workshop in 2013",

Genetic Programming entries for Terence Soule Robert B Heckendorn