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Robot juggling: An implementation of memory-based learning




This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memory-based local modeling approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its prediction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements during exploration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast real-time learning of the task within 40 to 100 trials.

Author(s): Schaal, S. and Atkeson, C. G.
Book Title: Control Systems Magazine
Volume: 14
Number (issue): 1
Pages: 57-71
Year: 1994

Department(s): Autonomous Motion
Bibtex Type: Article (article)

Cross Ref: p865
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/schaal-CSM1994.pdf


  title = {Robot juggling: An implementation of memory-based learning},
  author = {Schaal, S. and Atkeson, C. G.},
  booktitle = {Control Systems Magazine},
  volume = {14},
  number = {1},
  pages = {57-71},
  year = {1994},
  note = {clmc},
  crossref = {p865},
  url = {http://www-clmc.usc.edu/publications/S/schaal-CSM1994.pdf}