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Sharpening haptic inputs for teaching a manipulation skill to a robot
Rozo Castañeda, Leonel; Jimenez Schlegl, Pablo; Torras, Carme
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. Institut de Robòtica i Informàtica Industrial
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.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Robòtica
GMM
GMR
Intelligent robots
Robot learning
Robot programming
Robots -- Design and construction
Robots -- Kinematics
intelligent robots robot programming telerobotics robot learning
GMM
GMR
mutual information
Robots -- Disseny i construcció
Robots -- Sistemes de control
Robots -- Cinemàtica
Classificació INSPEC::Automation::Robots::Robot programming
info:eu-repo/semantics/submittedVersion
info:eu-repo/semantics/conferenceObject
         

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