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Memory-based robot learning


Conference Paper


We present a memory-based local modeling approach to robot learning using a nonparametric regression technique, locally weighted regression. The model of the task to be performed is represented by infinitely many local linear models, the (hyper-) tangent planes at every query point. This is in contrast to other methods using a finite set of linear models to accomplish a piece-wise linear model. Architectural parameters of our approach, such as distance metrics, are a function of the current query point instead of being global. Statistical tests are presented for when a local model is good enough such that it can be reliably used to build a local controller. These statistical measures also direct the exploration of the robot. We explicitly deal with the case where prediction accuracy requirements exist during exploration: By gradually shifting a center of exploration and controlling the speed of the shift with local prediction accuracy, a goal-directed exploration of state space takes place along the fringes of the current data support until the task goal is achieved. We illustrate this approach by describing how it has been used to enable a robot to learn a challenging juggling task: within 40 to 100 trials the robot accomplished the task goal starting out with no initial experiences.

Author(s): Schaal, S. and Atkeson, C. G.
Book Title: IEEE International Conference on Robotics and Automation
Volume: 3
Pages: 2928-2933
Year: 1994

Department(s): Autonomous Motion
Bibtex Type: Conference Paper (inproceedings)

Address: San Diego, CA
Cross Ref: p868
Note: clmc


  title = {Memory-based robot learning},
  author = {Schaal, S. and Atkeson, C. G.},
  booktitle = {IEEE International Conference on Robotics and Automation},
  volume = {3},
  pages = {2928-2933},
  address = {San Diego, CA},
  year = {1994},
  note = {clmc},
  crossref = {p868}