000143506 001__ 143506
000143506 005__ 20180913055607.0
000143506 037__ $$aCONF
000143506 245__ $$aEvaluation of a probabilistic approach to learn and  reproduce gestures by imitation
000143506 260__ $$c2010
000143506 269__ $$a2010
000143506 336__ $$aConference Papers
000143506 520__ $$aWe present an approach based on Hidden Markov Model (HMM)  and Gaussian Mixture Regression (GMR) to learn robust models  of human motion through imitation. The proposed approach  allows us to extract redundancies across multiple  demonstrations and build time-independent models to  reproduce the dynamics of the demonstrated movements. The  approach is systematically evaluated by using automatically  generated trajectories sharing similarities with human  gestures, and by using several metrics to assess the  imitation performance. The proposed approach is contrasted  with four state-of-the-art methods previously proposed in  robotics to learn and reproduce new skills by imitation. An  experiment with a 7 DOFs robotic arm learning and  reproducing the motion of hitting a ball with a table tennis  racket is then presented to illustrate the approach.
000143506 6531_ $$aRobot programming by demonstration
000143506 6531_ $$aLearning by imitation
000143506 6531_ $$aDynamical systems
000143506 700__ $$0240592$$aCalinon, Sylvain$$g119190
000143506 700__ $$aSauser, Eric L.
000143506 700__ $$0240594$$aBillard, Aude G.$$g115671
000143506 700__ $$aCaldwell, Darwin G.
000143506 7112_ $$aIEEE Intl Conf. on Robotics and Automation (ICRA)$$cAnchorage, Alaska, USA$$dMay 3-8
000143506 773__ $$tProc. of the IEEE Intl Conf. on Robotics and Automation (ICRA)
000143506 909C0 $$0252119$$pLASA$$xU10660
000143506 909CO $$ooai:infoscience.tind.io:143506$$pconf$$pSTI
000143506 937__ $$aLASA-CONF-2010-004
000143506 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000143506 980__ $$aCONF