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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Use of Support Vector Regression in Stable Trajectory Generation for Walking Humanoid Robots

  • Kim, Dong-Won (Department of Electrical Engineering and Computer Sciences, University of California) ;
  • Seo, Sam-Jun (Department of Electrical and Electronic Engineering, Anyang University) ;
  • De Silva, Clarence W. (Department of Mechanical Engineering, University of British Columbia) ;
  • Park, Gwi-Tae (Department of Electrical Engineering, Korea University)
  • Received : 2008.08.13
  • Accepted : 2009.08.18
  • Published : 2009.10.31
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Abstract

This paper concerns the use of support vector regression (SVR), which is based on the kernel method for learning from examples, in identification of walking robots. To handle complex dynamics in humanoid robot and realize stable walking, this paper develops and implements two types of reference natural motions for a humanoid, namely, walking trajectories on a flat floor and on an ascending slope. Next, SVR is applied to model stable walking motions by considering these actual motions. Three kinds of kernels, namely, linear, polynomial, and radial basis function (RBF), are considered, and the results from these kernels are compared and evaluated. The results show that the SVR approach works well, and SVR with the RBF kernel function provides the best performance. Plus, it can be effectively applied to model and control a practical biped walking robot.

Keywords

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