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Accurate deformable object manipulation (DOM) is essential for achievingautonomy in robotic surgery, where soft tissues are being displaced, stretched,and dissected. Many DOM methods can be powered by simulation, which ensuresrealistic deformation by adhering to the governing physical constraints andallowing for model prediction and control. However, real soft objects inrobotic surgery, such as membranes and soft tissues, have complex, anisotropicphysical parameters that a simulation with simple initialization from camerasmay not fully capture. To use the simulation techniques in real surgical tasks,the "real-to-sim" gap needs to be properly compensated. In this work, wepropose an online, adaptive parameter tuning approach for simulationoptimization that (1) bridges the real-to-sim gap between a physics simulationand observations obtained 3D perceptions through estimating a residual mappingand (2) optimizes its stiffness parameters online. Our method ensures a smallresidual gap between the simulation and observation and improves thesimulation's predictive capabilities. The effectiveness of the proposedmechanism is evaluated in the manipulation of both a thin-shell and volumetrictissue, representative of most tissue scenarios. This work contributes to theadvancement of simulation-based deformable tissue manipulation and holdspotential for improving surgical autonomy.

Real-to-Sim Deformable Object Manipulation: Optimizing Physics Models with Residual Mappings for Robotic Surgery