For the shape control of deformable free-form surfaces, simulation plays a crucial role in establishing the mapping between the actuation parameters and the deformed shapes. The differentiation of this forward kinematic mapping is usually employed to solve the inverse kinematic problem for determining the actuation parameters that can realize a target shape. However, the free-form surfaces obtained from simulators are always different from the physically deformed shapes due to the errors introduced by hardware and the simplification adopted in physical simulation. To fill the gap, we propose a novel deformation function based sim-to-real learning method that can map the geometric shape of a simulated model into its corresponding shape of the physical model. Unlike the existing sim-to-real learning methods that rely on completely acquired dense markers, our method accommodates sparsely distributed markers and can resiliently use all captured frames -- even for those in the presence of missing markers. To demonstrate its effectiveness, our sim-to-real method has been integrated into a neural network-based computational pipeline designed to tackle the inverse kinematic problem on a pneumatically actuated deformable mannequin.

翻译：对于可变形自由曲面的形状控制，仿真在建立驱动参数与变形形状之间的映射中起着关键作用。前向运动学映射的微分通常用于求解逆运动学问题，以确定能够实现目标形状的驱动参数。然而，由于硬件引入的误差以及物理仿真中采用的简化，从仿真器获得的自由曲面与实际物理变形形状总是存在差异。为弥合这一差距，我们提出了一种基于变形函数的模拟到现实学习方法，该方法能将仿真模型的几何形状映射到对应物理模型的形状。与现有依赖完全采集密集标记点的模拟到现实学习方法不同，我们的方法适用于稀疏分布标记点，并能弹性利用所有捕获帧——即使其中存在缺失标记点的情况。为验证其有效性，我们的模拟到现实方法已集成到基于神经网络的计算机管线中，该管线设计用于在气动驱动可变形人体模型上求解逆运动学问题。