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Configuration Depending Crosstalk Torque Calibration for Robotic Manipulators with Deep Neural Regression Models

  • Adrian ZwienerEmail author
  • Sebastian Otte
  • Richard Hanten
  • Andreas Zell
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

In this paper, an approach for articulated robotic manipulator which minimizes configuration depending crosstalk torques is presented. In particular, these crosstalk torques are an issue for the Kinova Jaco 2 manipulator. We can experimentally show that the presented approach leads to crosstalk minimization for the Kinova Jaco 2 manipulator. Crosstalk leads to a significant difference between sensor output and inverse dynamic models using CAD rigid body parameters. As a consequence, these disturbances lead to a hindered torque control and perception. Different machine learning techniques, namely Random Forests and various neural network architectures, are evaluated on this task. We show that particularly deep neural regression networks are able to learn the influence of the cross torques which improves perception.

Keywords

Crosstalk torques Calibration Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Adrian Zwiener
    • 1
    Email author
  • Sebastian Otte
    • 2
  • Richard Hanten
    • 1
  • Andreas Zell
    • 1
  1. 1.Cognitive Systems GroupUniversity of TübingenTübingenGermany
  2. 2.Cognitive Modeling GroupUniversity of TübingenTübingenGermany

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