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Bayesian robot system identification with input and output noise.

Jo-Anne Ting1, Aaron D'Souza, Stefan Schaal

  • 1University of British Columbia, Vancouver, BC, Canada. jting@acm.org

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Summary
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This study introduces a Bayesian system identification method for robot dynamics. The novel approach improves parameter estimation accuracy and robustness, even with noisy data.

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Area of Science:

  • Robotics
  • Machine Learning
  • System Identification

Background:

  • Model-based control is crucial for complex robots like humanoids, but traditional methods struggle with lightweight, multi-DOF systems due to nonlinear dynamics and noise.
  • Accurate robot dynamic models are essential for precise control and compliance, yet conventional CAD-based identification is often insufficient.
  • Data-driven methods face challenges with noisy measurements and potential for physically inconsistent results from unmodeled effects.

Purpose of the Study:

  • To develop a robust and accurate Bayesian system identification technique for estimating rigid body dynamics in robotic systems.
  • To address limitations of conventional and existing data-driven methods, particularly noise sensitivity and physical inconsistency.
  • To create a computationally efficient algorithm capable of handling ill-conditioned data and identifying noise characteristics.

Main Methods:

  • A variational Bayesian regression algorithm inspired by Factor Analysis regression was developed.
  • The method is designed to be robust to ill-conditioned data and automatically detect relevant features.
  • It specifically addresses and identifies both input and output noise in system measurements.

Main Results:

  • The proposed Bayesian system identification technique demonstrated significant improvements in rigid body parameter estimation for robotic systems.
  • Achieved up to three times lower error compared to current state-of-the-art machine learning methods.
  • The approach proved robust to noisy data and provided physically consistent parameter estimates.

Conclusions:

  • The developed Bayesian system identification technique offers a superior alternative for accurate robot dynamics modeling.
  • This method enhances the reliability and performance of model-based control in complex robotic systems.
  • The algorithm's efficiency and robustness make it suitable for real-world applications with noisy sensor data.