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International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 5/2022

06-04-2022 | Original Article

Transfer of learned dynamics between different surgical robots and operative configurations

Authors: Nural Yilmaz, Jintan Zhang, Peter Kazanzides, Ugur Tumerdem

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 5/2022

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Abstract

Purpose

Using the da Vinci Research Kit (dVRK), we propose and experimentally demonstrate transfer learning (Xfer) of dynamics between different configurations and robots distributed around the world. This can extend recent research using neural networks to estimate the dynamics of the patient side manipulator (PSM) to provide accurate external end-effector force estimation, by adapting it to different robots and instruments, and in different configurations, with additional forces applied on the instruments as they pass through the trocar.

Methods

The goal of the learned models is to predict internal joint torques during robot motion. First, exhaustive training is performed during free-space (FS) motion, using several configurations to include gravity effects. Second, to adapt to different setups, a limited amount of training data is collected and then the neural network is updated through Xfer.

Results

Xfer can adapt a FS network trained on one robot, in one configuration, with a particular instrument, to provide comparable joint torque estimation for a different robot, in a different configuration, using a different instrument, and inserted through a trocar. The robustness of this approach is demonstrated with multiple PSMs (sampled from the dVRK community), instruments, configurations and trocar ports.

Conclusion

Xfer provides significant improvements in prediction errors without the need for complete training from scratch and is robust over a wide range of robots, kinematic configurations, surgical instruments, and patient-specific setups.
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Metadata
Title
Transfer of learned dynamics between different surgical robots and operative configurations
Authors
Nural Yilmaz
Jintan Zhang
Peter Kazanzides
Ugur Tumerdem
Publication date
06-04-2022
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 5/2022
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-022-02601-7

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