The simulated evolution of robot perception

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@PhdThesis{Martin_2001_3875,
  author =       "Martin C. Martin",
  title =        "The simulated evolution of robot perception",
  school =       "Robotics Institute, Carnegie Mellon University",
  year =         "2001",
  address =      "Pittsburgh, PA, USA",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming",
  oai =          "oai:xtcat.oclc.org:OCLCNo/ocm48763718",
  URL =          "http://www.ri.cmu.edu/pub_files/pub3/martin_martin_c_2001_1/martin_martin_c_2001_1.pdf",
  URL =          "http://phdtree.org/pdf/23537150-the-simulated-evolution-of-robot-perception/",
  size =         "167 pages",
  abstract =     "This dissertation tackles the problem of using genetic
                 programming to create the vision subsystem of a
                 reactive obstacle avoidance algorithm for a mobile
                 robot. To focus the search on computationally efficient
                 algorithms while dealing images from a non-toy problem,
                 the representation restricts computation to be over a
                 window which moves vertically over the image. The
                 evolved programs estimated the distance to the nearest
                 object in various directions, given only a camera image
                 as input. Using a typical supervised learning
                 framework, images of the environment were collected
                 from the robot{'}s camera and the correct distance in
                 various directions determined by hand. Evolving
                 programs were evaluated on this fixed training set and
                 compared to the hand determined answers. Once the
                 evolution was complete, obstacle avoidance programs
                 were written to use the best evolved programs, and the
                 combined system used to control a robot.

                 The approach can be seen as automating the iterative
                 design process. A researcher{'}s main contribution is
                 typically at a high level -- techniques and frameworks
                 -- yet most time is spent on an example problem, trying
                 different instantiations until one works. When faced
                 with such a problem, one can usually think of a half
                 dozen very different approaches, and even write them
                 out in pseudo code. The technique proposed here can be
                 seen as searching the space spanned by that pseudo
                 code.

                 In a series of experiments, programs were evolved in
                 three different ways for two different environments to
                 both create working systems and push the limits of the
                 approach. Even in this nascent form, the evolved
                 programs work about as well as existing, hand written
                 systems. They used a number of architectures, including
                 a recurrent mathematical formula and a series of if
                 statements similar to a decision tree but with
                 non-linear relations between as many as five image
                 statistics. They successfully coded around
                 peculiarities of the imaging process and exploited
                 regularities of the environment. Finally, when given a
                 representation so general as to cause the genetic
                 algorithm to fail, and hand constructed rough answer
                 was used as a {'}seed,{'} which the genetic algorithm
                 successively modified to cut its error rate by a factor
                 of 5.8. This dissertation grew out of my conviction
                 that critiques of Artificial Intelligence can be viewed
                 constructively, as intellectual lighthouses to guide us
                 closer to the fundamental nature of thought, to the
                 real problems at the heart of intelligence. To not
                 address them, to work on techniques with fundamental
                 flaws, would be fooling oneself no matter how
                 impressive the demonstrations. There seems to be
                 something fundamental about AI that we are all missing,
                 and I believe these critiques bring us closer to
                 it.

                 This dissertation describes the experiments and their
                 results, discusses ways to develop them further, then
                 presents critiques of AI and discusses the potential of
                 this approach to overcome those critiques.",
  notes =        "http://www.ri.cmu.edu/pubs/pub_3875.html#text_ref

                 Martin Charles Martin",
}

Genetic Programming entries for Martin C Martin

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