This paper describes the trajectory learning component of a programming by demonstration (PbD) system for manipulation tasks. In case of multiple user demonstrations, the proposed approach clusters a set of hand trajectories and recovers smooth robot trajectories overcoming sensor noise and human motion inconsistency problems. More specifically, we integrate a geometric approach for trajectory clustering with a stochastic procedure for trajectory evaluation based on hidden Markov models. Furthermore, we propose a method for human hand trajectory reconstruction with NURBS curves by means of a best-fit data smoothing algorithm. Some experiments show the viability and effectiveness of the approach.
Trajectory Clustering and Stochastic Approximation for Robot Programming by Demonstration / Aleotti, Jacopo; Caselli, Stefano. - (2005), pp. 1029-1034. (Intervento presentato al convegno IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'2005) tenutosi a Edmonton, Canada nel 2-6 August 2005) [10.1109/IROS.2005.1545365].
Trajectory Clustering and Stochastic Approximation for Robot Programming by Demonstration
ALEOTTI, Jacopo;CASELLI, Stefano
2005-01-01
Abstract
This paper describes the trajectory learning component of a programming by demonstration (PbD) system for manipulation tasks. In case of multiple user demonstrations, the proposed approach clusters a set of hand trajectories and recovers smooth robot trajectories overcoming sensor noise and human motion inconsistency problems. More specifically, we integrate a geometric approach for trajectory clustering with a stochastic procedure for trajectory evaluation based on hidden Markov models. Furthermore, we propose a method for human hand trajectory reconstruction with NURBS curves by means of a best-fit data smoothing algorithm. Some experiments show the viability and effectiveness of the approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.