000140938 001__ 140938
000140938 005__ 20190316234616.0
000140938 037__ $$aCONF
000140938 245__ $$aLearning Nonlinear Multi-Variate Motion Dynamics for Real- Time Position and Orientation Control of Robotic Manipulators
000140938 260__ $$c2009
000140938 269__ $$a2009
000140938 336__ $$aConference Papers
000140938 520__ $$aWe present a generic framework that allows learning non- linear dynamics of motion in manipulation tasks and generating dynamical laws for control of position and orientation. This work follows a recent trend in Programming by Demonstration in which the dynamics of an arm motion is learned: position and orientation control are learned as multivariate dynamical systems to preserve correlation within the signals. The strength of the method is three-fold: i) it extracts dynamical control laws from demonstrations, and subsequently provides concurrent smooth control of both position and orientation; ii) it allows to generalize a motion to unseen context; iii) it guarantees on-line adaptation of the motion in the face of spatial and temporal perturbations. The method is validated to control a four degree of freedom humanoid arm and an industrial six degree of freedom robotic arm.
000140938 700__ $$0243000$$g173137$$aGribovskaya, Elena
000140938 700__ $$0240594$$g115671$$aBillard, Aude
000140938 7112_ $$a9th IEEE-RAS International Conference on Humanoid Robots
000140938 773__ $$tProceedings of 9th IEEE-RAS International Conference on Humanoid Robots
000140938 8564_ $$zURL
000140938 8564_ $$uhttps://infoscience.epfl.ch/record/140938/files/Humanoids09_Gribovskaya_Billard.pdf$$zn/a$$s1230220
000140938 909C0 $$xU10660$$0252119$$pLASA
000140938 909CO $$qGLOBAL_SET$$pconf$$ooai:infoscience.tind.io:140938$$pSTI
000140938 937__ $$aLASA-CONF-2009-005
000140938 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000140938 980__ $$aCONF