Simulator keeps deformable tissue grasp stable for surgical robots
Contact-Stable Deformable Tissue Simulation Using Implicit Integration and Live-Pose Grasp Constraints for Laparoscopic Surgery Robot Policy Evaluation
Robotics
Summary
It's hard to test surgical robots realistically because soft tissues move and change when touched or lifted. The authors made a simulator that creates virtual organs from real video data and allows robot hands to grasp and lift them realistically without the virtual tissue breaking apart or slipping unnaturally. Their simulation uses special math methods that keep the tissue stable during interaction, matching how actual tissue might behave during surgery. This tool helps in testing and improving robot control policies safely before using real surgery robots.
What this means in practice
- •For surgical robot developers: Test and refine robot control policies for soft tissue handling using realistic simulations that maintain contact stability during grasp and lift.
- •For robotics simulation engineers: Build stable simulations of deformable biological tissues that avoid unrealistic snapping or slipping during contact and manipulation.
Authors
Juahn Oh, Dongho Yee, Jinseok Lee, Jiyul Lee, Yechan Seo, Seong Jeong, Minsung Kim, Seonho Shim, Younghoon Noh, Hyuk Choi, Youngbin Kong, Hyoun-Joong Kon
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.