The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Despite the large amount of research currently directed toward programming robots by demonstration, a significant problem with this method of human-to-robot skill transfer has not yet been addressed: developing representations of human performances which isolate the intrinsic dimensions of the performances (and thus the skills which guide them) within high-dimensional, raw human performance data. In this paper we propose the use of three methods for representing high-dimensional human performance data within lower-dimensional spaces: principal-component analysis (PCA), nonlinear principal-component analysis (NLPCA), and sequential nonlinear principal-component analysis (SNLPCA). We compare the appropriateness of these methods for modeling a simple human grasping operation.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference

Christopher Lee and Yangsheng Xu, "Reduced-dimension representations of human performance data for human-to-robot skill transfer," IEEE/RSJ International Conference on Intelligent Robotic Systems, October, 1998.

BibTeX Reference

@inproceedings{Lee_1998_1021,
   author = "Christopher Lee and Yangsheng Xu",
   title = "Reduced-dimension representations of human performance data for human-to-robot skill transfer",
   booktitle = "IEEE/RSJ International Conference on Intelligent Robotic Systems",
   month = "October",
   year = "1998",
}

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