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This research introduces a high-fidelity tissue simulator specifically designed for closed-loop evaluation of laparoscopic surgical robots, addressing critical stability issues in contact mechanics and grasp constraints. By utilizing implicit integration and live-pose capture, the system significantly reduces simulation artifacts like tissue snapping, enabling more accurate safety screening and policy validation for robotic arms operating through trocars. While the core methodology involves advanced physics-based modeling, its primary value lies in advancing the reliability of AI-driven surgical robotics rather than general machine learning theory.
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Contact-Stable Deformable Tissue Simulation Using Implicit Integration and Live-Pose Grasp Constraints for Laparoscopic Surgery Robot Policy Evaluation

arXiv3 min read

arXiv:2609.25642v1 Announce Type: new Abstract: Closed-loop evaluation of surgical robots requires tissue that deforms, can be grasped and lifted, and reproduces the anatomy in which the robot will operate. We present a simulator in which this tissue is reconstructed from a fixed-view RGB-D recording of the surgical field, composited to remove the instruments, closed into watertight volumes and tetrahedralised; the pipeline was applied unchanged to three specimens of two species (thirteen organs, 146,061 tetrahedra, no inverted elements). For one specimen, the organs are placed in a bimanual cell in which two Franka FR3 arms operate motorised instruments through 6 mm trocars. The core contribution is the numerical and contact design that keeps this cell stable: implicit integration, simulation meshes separate from collision meshes, numerical guards, and a grasp constraint captured at the live tissue pose. In 45 repeated grasp-lifts, a friction grasp held the tissue in 0 of 15 trials and each constraint grasp in 13 of 15; on displaced tissue, a rest-pose constraint produced one-step snaps of up to 17.8 mm, which live-pose capture eliminates. Against the recording, front-surface depth error is 1.33 to 1.41 mm, organ silhouette IoU is 0.80, and in five grasp-lifts reproduced from video the landmark displacement RMSE is 11.8 mm against 14.2 mm for a static prediction. Biofidelity is not claimed; the environment is intended for closed-loop feasibility, safety, contact and policy screening.

Abstract:Closed-loop evaluation of surgical robots requires tissue that deforms, can be grasped and lifted, and reproduces the anatomy in which the robot will operate. We present a simulator in which this tissue is reconstructed from a fixed-view RGB-D recording of the surgical field, composited to remove the instruments, closed into watertight volumes and tetrahedralised; the pipeline was applied unchanged to three specimens of two species (thirteen organs, 146,061 tetrahedra, no inverted elements). For one specimen, the organs are placed in a bimanual cell in which two Franka FR3 arms operate motorised instruments through 6 mm trocars. The core contribution is the numerical and contact design that keeps this cell stable: implicit integration, simulation meshes separate from collision meshes, numerical guards, and a grasp constraint captured at the live tissue pose. In 45 repeated grasp-lifts, a friction grasp held the tissue in 0 of 15 trials and each constraint grasp in 13 of 15; on displaced tissue, a rest-pose constraint produced one-step snaps of up to 17.8 mm, which live-pose capture eliminates. Against the recording, front-surface depth error is 1.33 to 1.41 mm, organ silhouette IoU is 0.80, and in five grasp-lifts reproduced from video the landmark displacement RMSE is 11.8 mm against 14.2 mm for a static prediction. Biofidelity is not claimed; the environment is intended for closed-loop feasibility, safety, contact and policy screening.

Robotics (cs.RO)

arXiv:2609.25642 [cs.RO]

(or arXiv:2609.25642v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.25642 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 317 words

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