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：精确的可变形物体操控（DOM）对于实现机器人手术的自主性至关重要，其中软组织被位移、拉伸和切割。许多DOM方法可由仿真驱动，仿真通过遵循主导物理约束并允许模型预测与控制来确保逼真的变形。然而，机器人手术中的真实柔软物体（如膜状组织和软组织）具有复杂、各向异性的物理参数，仅通过相机简单初始化的仿真可能无法完全捕捉。为了在真实手术任务中使用仿真技术，需要适当补偿“真实到仿真”的差距。在本工作中，我们提出了一种在线自适应参数调优方法用于仿真优化，该方法（1）通过估计残差映射，弥合物理仿真与通过三维感知获得的观测之间的真实到仿真差距；（2）在线优化其刚度参数。我们的方法确保仿真与观测之间的残差较小，并提升了仿真的预测能力。所提出机制的有效性在薄壳组织和体积组织（代表大多数组织场景）的操控中进行了评估。这项工作有助于推进基于仿真的可变形组织操控，并具有提升手术自主性的潜力。