Deep Learning with Transfer Learning Method for Error Compensation of Cable-driven Robot

Aydar Akhmetzyanov, Maksim Rassabin, Alexander Maloletov, Mikhail Fadeev, Alexandr Klimchik


This paper proposes the application of Deep Learning methods for kinematic error compensation. Particular attention is paid to simulation-based error estimation and the use of the Transfer Learning method for error compensation to reduce physical experiments with a real robot. The obtained results were applied and validated for 4-dof (degrees of freedom) cable-driven parallel robot. The problem of error compensation for the cable-driven parallel robot is highly non-linear. Nevertheless, deep learning-based methods for a considerable training dataset provides better accuracy than simple linear error compensators. To overcome this drawback, we applied the transfer learning method and used the knowledge of robot kinematics simulated in Unity. Unity cable-driven robot simulation was implemented, and the central hypothesis was verified first in the simulated environment. The proposed Transfer Learning method allowed to speed up the process of robotics system integration and recalibration due to the significant sample efficiency improvement.


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