Name
Abstract | ||
|---|---|---|
In this paper we present a new methodology to learn and integrate different movement primitives in real-time. Our approach starts from a library of example trajectories for each primitive movement, which serves as a basis for the generation of a complete representation for the trained movement primitives by statistical generalization. To enable fast switching between different movement primitives, it is essential that on-line calculations needed to initialize and switch to a new movement primitive are done in real-time. We show that by converting the initial trajectory data into dynamic systems, we can switch to a new movement primitive within a real-time sensory feedback loop. Experimentally we also show that the accuracy of the generalized movements is sufficient to realize tasks such as feedforward grasping. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/Humanoids.2011.6100845 | 2011 11th IEEE-RAS International Conference on Humanoid Robots |
Keywords | Field | DocType |
real-time generalization,trained movement primitive,statistical generalization,fast switching,online calculation,initial trajectory data,dynamic system,real time sensory feedback loop,generalized movement,feedforward grasping | Simulation,Control theory,Computer science,Feedback loop,Trajectory control,Grippers,Trajectory,Dynamical system,Feed forward,Statistical analysis | Conference |
ISSN | ISBN | Citations |
2164-0572 | 978-1-61284-866-2 | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Denis Forte | 1 | 28 | 1.69 |
| Ales Ude | 2 | 898 | 85.11 |
| Andrej Gams | 3 | 385 | 29.54 |