Publications Freek Stulp


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Compact Models of Motor Primitive Variations for Predictable Reaching and Obstacle Avoidance
Freek Stulp, Erhan Oztop, Peter Pastor, Michael Beetz, and Stefan Schaal. Compact Models of Motor Primitive Variations for Predictable Reaching and Obstacle Avoidance. In 9th IEEE-RAS International Conference on Humanoid Robots, 2009.
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Abstract
In most activities of daily living, related tasks are encountered over and over again. This regularity allows humans and robots to reuse existing solutions for known recurring tasks. We expect that reusing a set of standard solutions to solve similar tasks will facilitate the design and on-line adaptation of the control systems of robots operating in human environments. In this paper, we derive a set of standard solutions for reaching behavior from human motion data. We also derive stereotypical reaching trajectories for variations of the task, in which obstacles are present. These stereotypical trajectories are then compactly represented with Dynamic Movement Primitives. On the humanoid robot Sarcos CB, this approach leads to reproducible, predictable, and human-like reaching motions.
BibTeX
@InProceedings{stulp09compactmodels,
  title                    = {Compact Models of Motor Primitive Variations for Predictable Reaching and Obstacle Avoidance},
  author                   = {Freek Stulp and Erhan Oztop and Peter Pastor and Michael Beetz and Stefan Schaal},
  booktitle                = {9th IEEE-RAS International Conference on Humanoid Robots},
  year                     = {2009},
  abstract                 = {In most activities of daily living, related tasks are encountered over and over again. This regularity allows humans and robots to reuse existing solutions for known recurring tasks. We expect that reusing a set of standard solutions to solve similar tasks will facilitate the design and on-line adaptation of the control systems of robots operating in human environments. In this paper, we derive a set of standard solutions for reaching behavior from human motion data. We also derive stereotypical reaching trajectories for variations of the task, in which obstacles are present. These stereotypical trajectories are then compactly represented with Dynamic Movement Primitives. On the humanoid robot Sarcos CB, this approach leads to reproducible, predictable, and human-like reaching motions.},
  bib2html_pubtype         = {Refereed Conference Paper},
  bib2html_rescat          = {Imitation Learning and Regression}
}

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