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
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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“Recently, deep reinforcement learning has been used for autonomous robot control, e.g., self-driving vehicle [203], [199], medical robot [213], mobile service robot.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [213]
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“Recently, deep reinforcement learning has been used to control the surgical robot [213] for multilateral cutting in 2d orthotropic gauze.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [213]
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Metadata
- Authors
- Thananjeyan, B., Garg, A., Krishnan, S., Chen, C., Miller, L., & Goldberg, K.
- Affiliations
- University of California, Berkeley (US)
- Venue
- 2017 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 2371-2378
- ISSN
- 1050-4729 (ISSN-L)
- 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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