Simulating Surgical Robot Cutting of Thin Deformable Materials Using a Rope Grid Structure
Bibliographic record
Abstract
Traditional methods for autonomous cutting in surgical robotics have relied on trajectory-based planning algorithms. These methods fail to compensate for dynamic changes in soft materials such as deformation and topological change. To apply recent advances such as reinforcement learning (RL), a simulation is needed that models the cutting of soft materials. In this work, we develop a surgical robotics simulation environment for cutting deformable meshes with the da Vinci Research Kit (dVRK). Our environment is built using a particle-based physics simulation to simulate a rope grid structure to create realistic physics behavior and visual rendering. Cutting is implemented with the EndoWrist Round Tip Scissors (RTS) through a system of collision checking and callbacks to detect and update cuts. To showcase the deformable mesh cutting simulation, we design a cutting task of cutting along a desired path that can be solved through manual control. The grid structure can be adapted to render different materials, and we highlight how it can be made to resemble deformable tissue or fabric while being stable with no visible artifacts. This environment is a stepping stone towards training autonomous agents for cutting 2D deformable materials and building towards cutting more complex deformable shapes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".