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  A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics

Ting, J.-A., Mistry, M., Peters, J., Schaal, S., & Nakanishi, J. (2007). A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics. In G. Sukhatme, S. Schaal, W. Burgard, & D. Fox (Eds.), Robotics: Science and Systems II (pp. 247-254). Cambridge, MA, USA: MIT Press.

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Ting, J-A, Author
Mistry, M, Author
Peters, J1, Author              
Schaal, S, Author
Nakanishi, J, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: 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 10 to 30% 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.

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 Dates: 2007-04
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 5049
DOI: 10.15607/RSS.2006.II.032
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Title: Robotics: Science and Systems II (RSS 2006)
Place of Event: Philadelphia, PA, USA
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Title: Robotics: Science and Systems II
Source Genre: Proceedings
 Creator(s):
Sukhatme, GS, Editor
Schaal, S1, Editor            
Burgard, W, Editor
Fox, D, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 247 - 254 Identifier: ISBN: 0-262-69348-8