Publications Freek Stulp


Back to Homepage
Sorted by DateClassified by Publication TypeClassified by Research Category
Comparing Motion Generation and Motion Recall for Everyday Robotic Tasks
Carmen Lopera, Hilario Tomé, Adolfo Rodr\'iguez Tsouroukdissian, and Freek Stulp. Comparing Motion Generation and Motion Recall for Everyday Robotic Tasks. In 12th IEEE-RAS International Conference on Humanoid Robots, 2012.
Download
(unavailable)
Abstract
In a variety of problem domains, such as math and motion planning, humans use a dual strategy of generation and recall to find solutions. `Generation' uses production rules and models to search for novel solutions to novel problems, whereas `recall' reuses previously found solutions for similar previously encountered problems. As we expect the advantages of this dual strategy to carry over to the robotics domain, we compare and evaluate generation and recall strategies for motion planning on a set of reaching tasks. The specific implementations we use are the lazy variant of the Rapidly-exploring Random Trees and Dynamic Movement Primitives, and we compare these two methods on the commercially available REEM robot. Quantifying the differences and advantages of these methods constitutes is required to make informed decisions about which approach is most suitable for which application domain and task contexts.
BibTeX
@InProceedings{lopera12comparing,
  title                    = {Comparing Motion Generation and Motion Recall for Everyday Robotic Tasks},
  author                   = {Carmen Lopera and Hilario Tom\'e and Adolfo Rodr\'iguez Tsouroukdissian and Freek Stulp},
  booktitle                = {12th IEEE-RAS International Conference on Humanoid Robots},
  year                     = {2012},
  abstract                 = {In a variety of problem domains, such as math and motion planning, humans use a dual strategy of generation and recall to find solutions. `Generation' uses production rules and models to search for novel solutions to novel problems, whereas `recall' reuses previously found solutions for similar previously encountered problems. As we expect the advantages of this dual strategy to carry over to the robotics domain, we compare and evaluate generation and recall strategies for motion planning on a set of reaching tasks. The specific implementations we use are the lazy variant of the Rapidly-exploring Random Trees and Dynamic Movement Primitives, and we compare these two methods on the commercially available REEM robot. Quantifying the differences and advantages of these methods constitutes is required to make informed decisions about which approach is most suitable for which application domain and task contexts.},
  bib2html_pubtype         = {Refereed Conference Paper},
  bib2html_rescat          = {Reinforcement Learning of Robot Skills}
}

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints.


Generated by bib2html.pl (written by Patrick Riley ) on Mon Jul 20, 2015 21:50:11