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Sharpening haptic inputs for teaching a manipulation skill to a robot
dc.contributor.author | Rozo Castañeda, Leonel |
dc.contributor.author | Jiménez Schlegl, Pablo |
dc.contributor.author | Torras, Carme |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.date.accessioned | 2011-05-02T08:07:13Z |
dc.date.available | 2011-05-02T08:07:13Z |
dc.date.created | 2010 |
dc.date.issued | 2010 |
dc.identifier.citation | Rozo, L.; Jimenez, P.; Torras, C. Sharpening haptic inputs for teaching a manipulation skill to a robot. A: IEEE International Conference on Applied Bionics and Biomechanics. "1st IEEE International Conference on Applied Bionics and Biomechanics". Venice: 2010, p. 331-340. |
dc.identifier.uri | http://hdl.handle.net/2117/12439 |
dc.description.abstract | Gaussian mixtures-based learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstration (PbD). Input streams other than visual information, as used in most applications up to date, reveal themselves as quite useful in trajectory learning experiments where visual sources are not available. In this work we have used force/torque feedback through a haptic device for teaching a teleoperated robot to empty a rigid container. Structure vibrations and container inertia appeared to considerably disrupt the sensing process, so a filtering algorithm had to be devised. Moreover, some input variables seemed much more relevant to the particular task to be learned than others, which lead us to analyze the training data in order to select those relevant features through principal component analysis and a mutual information criterion. Then, a batch version of GMM/GMR [1], [2] was implemented using different training datasets (original, pre-processed data through PCA and MI). Tests where the teacher was instructed to follow a strategy compared to others where he was not lead to useful conclusions that permit devising the new research stages. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject.lcsh | GMM |
dc.subject.lcsh | GMR |
dc.subject.lcsh | Intelligent robots |
dc.subject.lcsh | Robot learning |
dc.subject.lcsh | Robot programming |
dc.subject.lcsh | Robots -- Design and construction |
dc.subject.lcsh | Robots -- Kinematics |
dc.subject.other | intelligent robots robot programming telerobotics robot learning |
dc.subject.other | GMM |
dc.subject.other | GMR |
dc.subject.other | mutual information |
dc.title | Sharpening haptic inputs for teaching a manipulation skill to a robot |
dc.type | Conference report |
dc.subject.lemac | Robots -- Disseny i construcció |
dc.subject.lemac | Robots -- Sistemes de control |
dc.subject.lemac | Robots -- Cinemàtica |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.inspec | Classificació INSPEC::Automation::Robots::Robot programming |
dc.relation.publisherversion | http://www.icabb-iss.org/ |
dc.rights.access | Open Access |
local.identifier.drac | 5003389 |
dc.description.version | Postprint (author’s final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/247947/EU/Gardening with a Cognitive System/GARNICS |
local.citation.author | Rozo, L.; Jimenez, P.; Torras, C. |
local.citation.contributor | IEEE International Conference on Applied Bionics and Biomechanics |
local.citation.pubplace | Venice |
local.citation.publicationName | 1st IEEE International Conference on Applied Bionics and Biomechanics |
local.citation.startingPage | 331 |
local.citation.endingPage | 340 |