Header logo is ei

A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics

2007

Conference Paper

ei


For robots of increasing complexity such as humanoid robots, conventional identification of rigid body dynamics models based on CAD data and actuator models becomes difficult and inaccurate due to the large number of additional nonlinear effects in these systems, e.g., stemming from stiff wires, hydraulic hoses, protective shells, skin, etc. Data driven parameter estimation offers an alternative model identification method, but it is often burdened by various other problems, such as significant noise in all measured or inferred variables of the robot. The danger of physically inconsistent results also exists due to unmodeled nonlinearities or insufficiently rich data. In this paper, we address all these problems by developing a Bayesian parameter identification method that can automatically detect noise in both input and output data for the regression algorithm that performs system identification. A post-processing step ensures physically consistent rigid body parameters by nonlinearly projecting the result of the Bayesian estimation onto constraints given by positive definite inertia matrices and the parallel axis theorem. We demonstrate on synthetic and actual robot data that our technique performs parameter identification with 5 to 20% higher accuracy than traditional methods. Due to the resulting physically consistent parameters, our algorithm enables us to apply advanced control methods that algebraically require physical consistency on robotic platforms.

Author(s): Ting, J-A. and Mistry, M. and Peters, J. and Schaal, S. and Nakanishi, J.
Book Title: RSS 2006
Journal: Robotics: Science and Systems II
Pages: 247-254
Year: 2007
Month: April
Day: 0
Editors: Sukhatme, G. S., S. Schaal, W. Burgard, D. Fox
Publisher: MIT Press

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

Event Name: Robotics: Science and Systems II (RSS 2006)
Event Place: Philadelphia, PA, USA

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{5049,
  title = {A Bayesian Approach to Nonlinear Parameter
  Identification for Rigid Body Dynamics},
  author = {Ting, J-A. and Mistry, M. and Peters, J. and Schaal, S. and Nakanishi, J.},
  journal = {Robotics: Science and Systems II},
  booktitle = {RSS 2006},
  pages = {247-254},
  editors = {Sukhatme, G. S., S. Schaal, W. Burgard, D. Fox},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = apr,
  year = {2007},
  month_numeric = {4}
}