Header logo is ei

Learning Robot Dynamics for Computed Torque Control Using Local Gaussian Processes Regression

2008

Conference Paper

ei


Accurate models of the robot dynamics allow the design of significantly more precise, energy-efficient and more compliant computed torque control for robots. However, in some cases the accuracy of rigid-body models does not suffice for sound control performance due to unmodeled nonlinearities such as hydraulic cables, complex friction, or actuator dynamics. In such cases, learning the models from data poses an interesting alternative and estimating the dynamics model using regression techniques becomes an important problem. However, the most accurate regression methods, e.g. Gaussian processes regression (GPR) and support vector regression (SVR), suffer from exceptional high computational complexity which prevents their usage for large numbers of samples or online learning to date. We proposed an approximation to the standard GPR using local Gaussian processes models. Due to reduced computational cost, local Gaussian processes (LGP) is capable for an online learning. Comparisons with other nonparametric regre ssions, e.g. standard GPR, SVR and locally weighted projection regression (LWPR), show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and SVR while being sufficiently fast for online learning.

Author(s): Nguyen-Tuong, D. and Peters, J.
Book Title: LAB-RS 2008
Journal: Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems (LAB-RS 2008)
Pages: 59-64
Year: 2008
Month: August
Day: 0
Editors: Stoica, A. , E. Tunstel, T. Huntsberger, T. Arslan, S. Vijayakumar, A. O. El-Rayis
Publisher: IEEE Computer Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/LAB-RS.2008.16
Event Name: 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems
Event Place: Edinburgh, Scotland

Address: Los Alamitos, CA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{5415,
  title = {Learning Robot Dynamics for Computed Torque Control Using Local Gaussian Processes Regression},
  author = {Nguyen-Tuong, D. and Peters, J.},
  journal = {Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems (LAB-RS 2008)},
  booktitle = {LAB-RS 2008},
  pages = {59-64},
  editors = {Stoica, A. , E. Tunstel, T. Huntsberger, T. Arslan, S. Vijayakumar, A. O. El-Rayis},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = aug,
  year = {2008},
  month_numeric = {8}
}