Sparse Summarization of Robotic Grasping Data
2013 (English)In: 2013 IEEE International Conference on Robotics and Automation (ICRA), New York: IEEE , 2013, 1082-1087 p.Conference paper (Refereed)
We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient representations of grasping data for two robotic scenarios.
Place, publisher, year, edition, pages
New York: IEEE , 2013. 1082-1087 p.
, IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Principal Component Analysis, Models
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-136187DOI: 10.1109/ICRA.2013.6630707ISI: 000337617301013ScopusID: 2-s2.0-84887316002ISBN: 978-1-4673-5643-5ISBN: 978-1-4673-5641-1OAI: oai:DiVA.org:kth-136187DiVA: diva2:675562
2013 IEEE International Conference on Robotics and Automation, ICRA 2013; Karlsruhe; Germany; 6 May 2013 through 10 May 2013
QC 201401292013-12-042013-12-042014-08-04Bibliographically approved