The deployment of complex soft robots in multiphysics environments requires advanced simulation frameworks that not only capture interactions between different types of material, but also translate accurately to real-world performance. Soft robots pose unique modeling challenges due to their large nonlinear deformations, material incompressibility, and contact interactions, which complicate both numerical stability and physical accuracy. Despite recent progress, robotic simulators often struggle with modeling such phenomena in a scalable and application-relevant manner. We present SORS (Soft Over Rigid Simulator), a versatile, high-fidelity simulator designed to handle these complexities for soft robot applications. Our energy-based framework, built on the finite element method, allows modular extensions, enabling the inclusion of custom-designed material and actuation models. To ensure physically consistent contact handling, we integrate a constrained nonlinear optimization based on sequential quadratic programming, allowing for stable and accurate modeling of contact phenomena. We validate our simulator through a diverse set of real-world experiments, which include cantilever deflection, pressure-actuation of a soft robotic arm, and contact interactions from the PokeFlex dataset. In addition, we showcase the potential of our framework for control optimization of a soft robotic leg. These tests confirm that our simulator can capture both fundamental material behavior and complex actuation dynamics with high physical fidelity. By bridging the sim-to-real gap in these challenging domains, our approach provides a validated tool for prototyping next-generation soft robots, filling the gap of extensibility, fidelity, and usability in the soft robotic ecosystem.

翻译：在复杂多物理场环境中部署软体机器人需要先进的仿真框架，这些框架不仅要捕捉不同类型材料之间的相互作用，还需准确映射至实际性能。软体机器人因其大范围非线性形变、材料不可压缩性及接触交互作用而带来独特的建模挑战，这些因素同时影响了数值稳定性与物理精度。尽管近期有所进展，机器人仿真器在可扩展且贴合应用需求地建模此类现象方面仍面临困难。本文提出SORS（Soft Over Rigid Simulator），一种多功能、高保真度的仿真器，专为处理软体机器人应用中的这些复杂性而设计。我们基于有限元方法构建的能量框架支持模块化扩展，能够集成用户自定义的材料与驱动模型。为确保物理一致的接触处理，我们整合了基于序列二次规划的约束非线性优化方法，从而实现对接触现象的稳定精确建模。我们通过一系列多样化的真实世界实验验证了仿真器的性能，包括悬臂梁挠曲、软体机械臂的压力驱动以及PokeFlex数据集中的接触交互。此外，我们还展示了该框架在软体机器人腿部控制优化方面的潜力。这些测试证实，我们的仿真器能够以高物理保真度捕捉基础材料行为与复杂驱动动力学。通过在挑战性领域中弥合仿真与现实的差距，本方法为原型化新一代软体机器人提供了经过验证的工具，填补了软体机器人生态系统中可扩展性、保真度与可用性方面的空白。