Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery

Scheikl PM, Tagliabue E, Gyenes B, Wagner M, Dall'Alba D, Fiorini P, Mathis-Ullrich F (2023)

Publication Type: Journal article

Publication year: 2023

Journal

IEEE Robotics and Automation Letters IEEE

Book Volume: 8

Pages Range: 560-567

Journal Issue: 2

DOI: 10.1109/LRA.2022.3227873

Abstract

Automation holds the potential to assist surgeons in robotic interventions, shifting their mental work load from visuomotor control to high level decision making. Reinforcement learning has shown promising results in learning complex visuomotor policies, especially in simulation environments where many samples can be collected at low cost. A core challenge is learning policies in simulation that can be deployed in the real world, thereby overcoming the sim-to-real gap. In this letter, we bridge the visual sim-to-real gap with an image-based reinforcement learning pipeline based on pixel-level domain adaptation and demonstrate its effectiveness on an image-based task in deformable object manipulation. We choose a tissue retraction task because of its importance in clinical reality of precise cancer surgery. After training in simulation on domain-translated images, our policy requires no retraining to perform tissue retraction with a 50% success rate on the real robotic system using raw RGB images. Furthermore, our sim-to-real transfer method makes no assumptions on the task itself and requires no paired images. This letter introduces the first successful application of visual sim-to-real transfer for robotic manipulation of deformable objects in the surgical field, which represents a notable step towards the clinical translation of cognitive surgical robotics.

Authors with CRIS profile

Paul Scheikl
Franziska Mathis-Ullrich

Involved external institutions

University of Verona / Università degli Studi di Verona IT Italy (IT) Universitätsklinikum Heidelberg DE Germany (DE) Karlsruhe Institute of Technology (KIT) DE Germany (DE)

How to cite

APA:

Scheikl, P.M., Tagliabue, E., Gyenes, B., Wagner, M., Dall'Alba, D., Fiorini, P., & Mathis-Ullrich, F. (2023). Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery. IEEE Robotics and Automation Letters, 8(2), 560-567. https://doi.org/10.1109/LRA.2022.3227873

MLA:

Scheikl, Paul Maria, et al. "Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery." IEEE Robotics and Automation Letters 8.2 (2023): 560-567.

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