Accurate deformable object manipulation (DOM) is essential for achieving autonomy in robotic surgery, where soft tissues are being displaced, stretched, and dissected. Many DOM methods can be powered by simulation, which ensures realistic deformation by adhering to the governing physical constraints and allowing for model prediction and control. However, real soft objects in robotic surgery, such as membranes and soft tissues, have complex, anisotropic physical parameters that a simulation with simple initialization from cameras may 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, we propose an online, adaptive parameter tuning approach for simulation optimization that (1) bridges the real-to-sim gap between a physics simulation and observations obtained 3D perceptions through estimating a residual mapping and (2) optimizes its stiffness parameters online. Our method ensures a small residual gap between the simulation and observation and improves the simulation's predictive capabilities. The effectiveness of the proposed mechanism is evaluated in the manipulation of both a thin-shell and volumetric tissue, representative of most tissue scenarios. This work contributes to the advancement of simulation-based deformable tissue manipulation and holds potential for improving surgical autonomy.

翻译：精确的变形物操作（DOM）是实现机器人手术自主性的关键，其中软组织被位移、拉伸和切割。许多DOM方法可借助仿真技术实现，通过遵循物理约束确保形变真实性，并支持模型预测与控制。然而，真实手术场景中的软物体（如薄膜与软组织）具有各向异性的复杂物理参数，仅凭相机初始化的简单仿真难以完全捕捉这些特性。为在真实手术任务中应用仿真技术，需妥善补偿"真实到模拟"的差异。本研究提出一种在线自适应参数调优方法用于仿真优化，该方法通过：（1）基于残差映射估计来弥合物理仿真与三维感知观测之间的真实到模拟差异；（2）在线优化其刚度参数。本方法确保仿真与观测间保持微小残差，并提升仿真的预测能力。通过薄膜与体积组织（涵盖多数组织场景）的操作实验验证了所提机制的有效性。本研究推动了基于仿真的变形组织操作技术发展，并具有提升手术自主性的潜力。