Physics-based simulation is essential for developing and evaluating robot manipulation policies, particularly in scenarios involving deformable objects and complex contact interactions. However, existing simulators often struggle to balance computational efficiency with numerical accuracy, especially when modeling deformable materials with frictional contact constraints. We introduce an efficient subspace representation for the Incremental Potential Contact (IPC) method, leveraging model reduction to decrease the number of degrees of freedom. Our approach decouples simulation complexity from the resolution of the input model by representing elasticity in a low-resolution subspace while maintaining collision constraints on an embedded high-resolution surface. Our barrier formulation ensures intersection-free trajectories and configurations regardless of material stiffness, time step size, or contact severity. We validate our simulator through quantitative experiments with a soft bubble gripper grasping and qualitative demonstrations of placing a plate on a dish rack. The results demonstrate our simulator's efficiency, physical accuracy, computational stability, and robust handling of frictional contact, making it well-suited for generating demonstration data and evaluating downstream robot training applications.

翻译：基于物理的仿真是开发和评估机器人操作策略的关键，尤其是在涉及可变形物体和复杂接触交互的场景中。然而，现有仿真器往往难以在计算效率与数值精度之间取得平衡，特别是在模拟具有摩擦接触约束的可变形材料时。我们为增量势能接触（IPC）方法引入了一种高效的子空间表示，利用模型降维来减少自由度数量。我们的方法通过将弹性表示在低分辨率子空间中，同时在嵌入的高分辨率表面上保持碰撞约束，从而将仿真复杂度与输入模型的分辨率解耦。我们的势垒公式确保了无论材料刚度、时间步长或接触严重程度如何，都能生成无交叠的轨迹和构型。我们通过软体气泡夹爪抓取的定量实验以及将盘子放入碗架上的定性演示验证了我们的仿真器。结果表明，我们的仿真器具有高效性、物理准确性、计算稳定性以及对摩擦接触的鲁棒处理能力，使其非常适用于生成演示数据和评估下游机器人训练应用。