000143506 001__ 143506
000143506 005__ 20190117210527.0
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