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Real-time statistical learning for robotics and human augmentation

2001

Conference Paper

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Real-time modeling of complex nonlinear dynamic processes has become increasingly important in various areas of robotics and human augmentation. To address such problems, we have been developing special statistical learning methods that meet the demands of on-line learning, in particular the need for low computational complexity, rapid learning, and scalability to high-dimensional spaces. In this paper, we introduce a novel algorithm that possesses all the necessary properties by combining methods from probabilistic and nonparametric learning. We demonstrate the applicability of our methods for three different applications in humanoid robotics, i.e., the on-line learning of a full-body inverse dynamics model, an inverse kinematics model, and imitation learning. The latter application will also introduce a novel method to shape attractor landscapes of dynamical system by means of statis-tical learning.

Author(s): Schaal, S. and Vijayakumar, S. and D’Souza, A. and Ijspeert, A. and Nakanishi, J.
Book Title: International Symposium on Robotics Research
Year: 2001
Editors: Jarvis, R. A.;Zelinsky, A.

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

Address: Lorne, Victoria, Austrialia Nov.9-12
Cross Ref: p1490
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/schaal-ISRR2001.pdf

BibTex

@inproceedings{Schaal_ISRR_2001,
  title = {Real-time statistical learning for robotics and human augmentation},
  author = {Schaal, S. and Vijayakumar, S. and D'Souza, A. and Ijspeert, A. and Nakanishi, J.},
  booktitle = {International Symposium on Robotics Research},
  editors = {Jarvis, R. A.;Zelinsky, A.},
  address = {Lorne, Victoria, Austrialia Nov.9-12},
  year = {2001},
  note = {clmc},
  crossref = {p1490},
  url = {http://www-clmc.usc.edu/publications/S/schaal-ISRR2001.pdf}
}