Name
Abstract | ||
|---|---|---|
Dynamical Systems (DS) for robot motion modeling are a promising approach for efficient robot learning and control. Our focus in this paper is on autonomous dynamical systems, which represent a motion plan without dependency on time. We develop a method that allows to locally reshape an existing, stable nonlinear autonomous DS while preserving important stability properties of the original system. Our system is based on local transformations of the dynamics. We propose an incremental learning algorithm based on Gaussian Processes for learning to reshape dynamical systems using this representation. The approach is validated in a 2d task of learning handwriting motions, a periodic polishing motion and in a manipulation task with the 7 degrees of freedom Barrett WAM manipulator. A novel Dynamical Systems formulation based on reshaping an existing system is introduced.The system can be flexibly reshaped to accommodate demonstrations.Stability properties of the original dynamics are retained in the reshaped dynamics.Evaluation on several robot manipulation tasks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.robot.2015.03.010 | Robotics and Autonomous Systems |
Keywords | Field | DocType |
Dynamical systems,Motion modeling,Robotics | Robot learning,Nonlinear system,Handwriting,Simulation,Computer science,Dynamical systems theory,Gaussian process,Artificial intelligence,Robot,Periodic graph (geometry),Robotics | Journal |
Volume | Issue | ISSN |
70 | C | 0921-8890 |
Citations | PageRank | References |
14 | 0.68 | 28 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Klas Kronander | 1 | 136 | 6.91 |
| Mohammad Khansari | 2 | 14 | 0.68 |
| Aude Billard | 3 | 3316 | 254.98 |