Breaking Out of the Black Box: A New Approach to Robot Perception

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  author =       "Martin C. Martin",
  title =        "Breaking Out of the Black Box: A New Approach to Robot
  school =       "Robotics Institute, Carnegie Mellon University",
  month =        jan,
  year =         "1998",
  address =      "Pittsburgh, PA, USA",
  note =         "Thesis proposal",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  size =         "28 pages",
  abstract =     "Surprisingly, the state of the art in avoiding
                 obstacles using only vision--not sonar or laser
                 rangefinders--is roughly half an hour between
                 collisions (at 30 cm/s, in an office environment).
                 After review ing the design and failure modes of
                 several current systems, I compare psychology's
                 understanding of perception to current computer/robot
                 perception. There are fundamental differences--which
                 lead to fundamental limitations with current computer
                 perception. The key difference is that robot software
                 is built out of {"}black boxes{"}, which have very
                 restricted interactions with each other. In contrast,
                 the human perceptual system is much more integrated.
                 The claim is that a robot that performs any significant
                 task, and does it as well as a person, can not be
                 created out of {"}black boxes.{"} In fact, it would
                 probably be too interconnected to be designed by
                 hand--instead, tools will be needed to create such
                 designs. To illustrate this idea, I propose to create a
                 visual obstacle avoidence system on the Uranus mobile
                 robot. The system uses a number of visual depth cues at
                 each pixel, as well as depth cues from neighbouring
                 pixels and previous depth estimates. Genetic
                 Programming is used to combine these into a new depth
                 estimate. The system learns by predicting both sonar
                 readings and the next image. The design of the system
                 is described, and design decisions are rationalized.",
  notes =        "see also",

Genetic Programming entries for Martin C Martin