000223034 001__ 223034
000223034 005__ 20180913064005.0
000223034 037__ $$aCONF
000223034 245__ $$aLearning dynamic graffiti strokes with a compliant robot
000223034 269__ $$a2016
000223034 260__ $$c2016
000223034 336__ $$aConference Papers
000223034 520__ $$aWe present an approach to generate rapid and fluid drawing movements on a compliant Baxter robot, by taking advantage of the kinematic redundancy and torque control capabilities of the robot. We concentrate on the task of reproducing graffiti-stylised letter-forms with a marker. For this purpose, we exploit a compact lognormal-stroke based representation of movement to generate natural drawing trajectories. An Expectation-Maximisation (EM) algorithm is used to iteratively improve tracking performance with low gain feedback control. The resulting system captures the aesthetic and dynamic features of the style under investigation and permits its reproduction with a compliant controller that is safe for users surrounding the robot.
000223034 6531_ $$amotion synthesis
000223034 6531_ $$arobot learning
000223034 700__ $$aBerio, D.
000223034 700__ $$aCalinon, S.
000223034 700__ $$aLeymarie, F. F.
000223034 7112_ $$aProc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems
000223034 8564_ $$uhttp://iros2016.org/$$zURL
000223034 909C0 $$0252189$$pLIDIAP$$xU10381
000223034 909CO $$ooai:infoscience.tind.io:223034$$pconf$$pSTI
000223034 937__ $$aEPFL-CONF-223034
000223034 970__ $$aBerio_IROS_2016/LIDIAP
000223034 980__ $$aCONF