Real-time adaptation technique to real robots: An experiment with a humanoid robot

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Shotaro Kamio and Hitoshi Iba",
  title =        "Real-time adaptation technique to real robots: An
                 experiment with a humanoid robot",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "506--513",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  ISBN =         "0-7803-7804-0",
  keywords =     "genetic algorithms, genetic programming, Costs,
                 Humanoid robots, Light sources, Machine learning,
                 Manufacturing processes, Neural networks, Robot
                 control, Robot programming, adaptive systems, learning
                 (artificial intelligence), real-time systems, robots,
                 task analysis, AIBO, HOAP-1 robot, Q-learning method,
                 box-moving task, humanoid robot, operational
                 characteristics, real robots, real-time adaptation,
                 real-time learning, reinforcement learning",
  DOI =          "doi:10.1109/CEC.2003.1299618",
  abstract =     "We introduce a technique that allows a real robot to
                 execute a real-time learning, in which GP and RL are
                 integrated. In our former research, we showed the
                 result of an experiment with a real robot 'AIBO' and
                 proved the technique performed better than the
                 traditional Q-learning method. Based on the proposed
                 technique, we can acquire the common programs using a
                 GP, applicable to various types of robots. We execute
                 reinforcement learning with the acquired program in a
                 real robot. In this way, the robot can adapt to its own
                 operational characteristics and learn effective
                 actions. In this paper, we show the experimental
                 results in which a humanoid robot HOAP-1 has been
                 evolved to perform effectively to solve the box-moving
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",

Genetic Programming entries for Shotaro Kamio Hitoshi Iba