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Purpose Surgical simulations play an increasingly important role in surgeoneducation and developing algorithms that enable robots to perform surgicalsubtasks. To model anatomy, Finite Element Method (FEM) simulations have beenheld as the gold standard for calculating accurate soft-tissue deformation.Unfortunately, their accuracy is highly dependent on the simulation parameters,which can be difficult to obtain.  Methods In this work, we investigate how live data acquired during anyrobotic endoscopic surgical procedure may be used to correct for inaccurate FEMsimulation results. Since FEMs are calculated from initial parameters andcannot directly incorporate observations, we propose to add a correction factorthat accounts for the discrepancy between simulation and observations. We traina network to predict this correction factor.  Results To evaluate our method, we use an open-source da Vinci SurgicalSystem to probe a soft-tissue phantom and replay the interaction in simulation.We train the network to correct for the difference between the predicted meshposition and the measured point cloud. This results in 15-30% improvement inthe mean distance, demonstrating the effectiveness of our approach across alarge range of simulation parameters.  Conclusion We show a first step towards a framework that synergisticallycombines the benefits of model-based simulation and real-time observations. Itcorrects discrepancies between simulation and the scene that results frominaccurate modeling parameters. This can provide a more accurate simulationenvironment for surgeons and better data with which to train algorithms.

Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery