Name
Title | ||
|---|---|---|
Trajectory clustering and stochastic approximation for robot programming by demonstration |
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/IROS.2005.1545365 | Intelligent Robots and Systems, 2005. |
Keywords | Field | DocType |
gesture recognition,hidden Markov models,learning by example,manipulator kinematics,object recognition,robot programming,robot vision,NURBS curves,best-fit data smoothing,hidden Markov model,human hand trajectory reconstruction,manipulation tasks,robot programming by demonstration,stochastic approximation,trajectory clustering,trajectory learning | Programming by demonstration,Computer vision,Computer science,Markov model,Gesture recognition,Smoothing,Artificial intelligence,Robot,Hidden Markov model,Stochastic approximation,Trajectory | Conference |
ISBN | Citations | PageRank |
0-7803-8912-3 | 17 | 0.77 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacopo Aleotti | 1 | 259 | 29.76 |
| Stefano Caselli | 2 | 314 | 36.32 |