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Autonomous framework for segmenting robot trajectories of manipulation task

Lee, Sang Hyoung; Suh, Il Hong; Calinon, Sylvain and Johansson, Rolf LU (2015) In Autonomous Robots 38(2). p.107-141
Abstract
In manipulation tasks, motion trajectories are characterized by a set of key phases (i.e., motion primitives). It is therefore important to learn the motion primitives embedded in such tasks from a complete demonstration. In this paper, we propose a core framework that autonomously segments motion trajectories to support the learning of motion primitives. For this purpose, a set of segmentation points is estimated using a Gaussian Mixture Model (GMM) learned after investigating the dimensional subspaces reduced by Principal Component Analysis. The segmentation points can be acquired by two alternative approaches: (1) using a geometrical interpretation of the Gaussians obtained from the learned GMM, and (2) using the weights estimated along... (More)
In manipulation tasks, motion trajectories are characterized by a set of key phases (i.e., motion primitives). It is therefore important to learn the motion primitives embedded in such tasks from a complete demonstration. In this paper, we propose a core framework that autonomously segments motion trajectories to support the learning of motion primitives. For this purpose, a set of segmentation points is estimated using a Gaussian Mixture Model (GMM) learned after investigating the dimensional subspaces reduced by Principal Component Analysis. The segmentation points can be acquired by two alternative approaches: (1) using a geometrical interpretation of the Gaussians obtained from the learned GMM, and (2) using the weights estimated along the time component of the learned GMM. The main contribution of this paper is the autonomous estimation of the segmentation points based on the GMM learned in a reduced dimensional space. The advantages of such an estimation are as follows: (1) segmentation points without any internal parameters to be manually predefined or pretuned (according to the types of given tasks and/or motion trajectories) can be estimated from a single training data, (2) segmentation points, in which non-linear motion trajectories can be better characterized than by using the original motion trajectories, can be estimated, and (3) natural motion trajectories can be retrieved by temporally rearranging motion segments. The capability of this autonomous segmentation framework is validated by four experiments. In the first experiment, motion segments are evaluated through a comparison with a human expert using a publicly available kitchen dataset. In the second experiment, motion segments are evaluated through a comparison with an existing approach using an open hand-writing database. In the third experiment, the segmentation performance is evaluated by retrieving motion trajectories from the reorganization of motion segments. In the fourth experiment, the segmentation performance is evaluated by clustering motion segments. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Autonomous segmentation, Motion segments, Gaussian mixture model, Principal component analysis
in
Autonomous Robots
volume
38
issue
2
pages
107 - 141
publisher
Springer
external identifiers
  • wos:000348128300001
  • scopus:84921699541
ISSN
0929-5593
DOI
10.1007/s10514-014-9397-9
language
English
LU publication?
yes
id
f87bdaac-93ea-45b8-9090-c2489c015def (old id 5053327)
date added to LUP
2015-02-24 09:36:15
date last changed
2017-10-29 04:01:48
@article{f87bdaac-93ea-45b8-9090-c2489c015def,
  abstract     = {In manipulation tasks, motion trajectories are characterized by a set of key phases (i.e., motion primitives). It is therefore important to learn the motion primitives embedded in such tasks from a complete demonstration. In this paper, we propose a core framework that autonomously segments motion trajectories to support the learning of motion primitives. For this purpose, a set of segmentation points is estimated using a Gaussian Mixture Model (GMM) learned after investigating the dimensional subspaces reduced by Principal Component Analysis. The segmentation points can be acquired by two alternative approaches: (1) using a geometrical interpretation of the Gaussians obtained from the learned GMM, and (2) using the weights estimated along the time component of the learned GMM. The main contribution of this paper is the autonomous estimation of the segmentation points based on the GMM learned in a reduced dimensional space. The advantages of such an estimation are as follows: (1) segmentation points without any internal parameters to be manually predefined or pretuned (according to the types of given tasks and/or motion trajectories) can be estimated from a single training data, (2) segmentation points, in which non-linear motion trajectories can be better characterized than by using the original motion trajectories, can be estimated, and (3) natural motion trajectories can be retrieved by temporally rearranging motion segments. The capability of this autonomous segmentation framework is validated by four experiments. In the first experiment, motion segments are evaluated through a comparison with a human expert using a publicly available kitchen dataset. In the second experiment, motion segments are evaluated through a comparison with an existing approach using an open hand-writing database. In the third experiment, the segmentation performance is evaluated by retrieving motion trajectories from the reorganization of motion segments. In the fourth experiment, the segmentation performance is evaluated by clustering motion segments.},
  author       = {Lee, Sang Hyoung and Suh, Il Hong and Calinon, Sylvain and Johansson, Rolf},
  issn         = {0929-5593},
  keyword      = {Autonomous segmentation,Motion segments,Gaussian mixture model,Principal component analysis},
  language     = {eng},
  number       = {2},
  pages        = {107--141},
  publisher    = {Springer},
  series       = {Autonomous Robots},
  title        = {Autonomous framework for segmenting robot trajectories of manipulation task},
  url          = {http://dx.doi.org/10.1007/s10514-014-9397-9},
  volume       = {38},
  year         = {2015},
}