Publication Details

SELECT * FROM publications WHERE Record_Number=2582
Reference TypeConference Proceedings
Author(s)Ting, J.;Mistry, M.;Nakanishi, J.;Peters, J.;Schaal, S.
Year2006
TitleA Bayesian approach to nonlinear parameter identification for rigid body dynamics
Journal/Conference/Book TitleRobotics: Science and Systems (RSS 2006)
KeywordsBayesian regression linear models dimensionality reduction input noise rigid body dynamics parameter identification
AbstractFor 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.
Place PublishedPhiladelphia, PA, Aug.16-19
PublisherCambridge, MA: MIT Press
Short TitleA Bayesian approach to nonlinear parameter identification for rigid body dynamics
URL(s) https://www.ias.informatik.tu-darmstadt.de/uploads/Publications/Publications/p32.pdf

  

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