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Cited by 144Surgical Simulation and TrainingReinforcement Learning in RoboticsSoft Robotics and Applications

The DOI matches its Crossref record; 36 of the 38 references it cites were found in a registry or an index.

Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning

Thananjeyan, Brijen, Garg, Animesh, Krishnan, Sanjay, Chen, Carolyn, Miller, Lauren, Goldberg, Ken·2017·2017 IEEE International Conference on Robotics and Automation (ICRA)
DOI 10.1109/icra.2017.7989275
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Abstract

In the Fundamentals of Laparoscopic Surgery (FLS) standard medical training regimen, the Pattern Cutting task requires residents to demonstrate proficiency by maneuvering two tools, surgical scissors and tissue gripper, to accurately cut a circular pattern on surgical gauze suspended at the corners. Accuracy of cutting depends on tensioning, wherein the gripper pinches a point on the gauze in R3and pulls to induce and maintain tension in the material as cutting proceeds. An automated tensioning policy maps the current state of the gauze to output a direction of pulling as an action. The optimal tensioning policy depends on both the choice of pinch point and cutting trajectory. We explore the problem of learning a tensioning policy conditioned on specific cutting trajectories. Every timestep, we allow the gripper to react to the deformation of the gauze and progress of the cutting trajectory with a translation unit vector along an allowable set of directions. As deformation is difficult to analytically model and explicitly observe, we leverage deep reinforcement learning with direct policy search methods to learn tensioning policies using a finite-element simulator and then transfer them to a physical system. We compare the Deep RL tensioning policies with fixed and analytic (opposing the error vector with a fixed pinch point) policies on a set of 17 open and closed curved contours in simulation and 4 patterns in physical experiments with the da Vinci Research Kit (dVRK). Our simulation results suggest that learning to tension with Deep RL can significantly improve performance and robustness to noise and external forces.

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  1. 2016, EndoWrist Instruments Catalogue in Intuitive Surgical, 3
  2. brooks, 2016, A tearable cloth simulation using vertlet integration

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Metadata

Authors
Thananjeyan, B., Garg, A., Krishnan, S., Chen, C., Miller, L., & Goldberg, K.
Affiliations
University of California, Berkeley (US)
ORCID
Venue
2017 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Published
2017-05
Pages
2371-2378
ISSN
1050-4729 (ISSN-L)
DOI
10.1109/icra.2017.7989275
OpenAlex
W2736750746
Cited by
144
References
38
Citations by year
2026: 5 · 2025: 13 · 2024: 22 · 2023: 20 · 2022: 18 · 2021: 25 · 2020: 21 · 2019: 10 · 2018: 7 · 2017: 3
FWCI
142.47
Type
conference paper
Language
en
Open access
closed
Locations
Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
Topics
  • Health Sciences › Medicine › Surgery › Surgical Simulation and Training
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
Keywords
pattern cutting, deep reinforcement learning, surgical training, Fundamentals of Laparoscopic Surgery, surgical scissors, finite element analysis, da Vinci Research Kit, policy transfer, cutting trajectory, surgical skill assessment

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