Differentiable physics simulation provides an avenue to tackle previously intractable challenges through gradient-based optimization, thereby greatly improving the efficiency of solving robotics-related problems. To apply differentiable simulation in diverse robotic manipulation scenarios, a key challenge is to integrate various materials in a unified framework. We present SoftMAC, a differentiable simulation framework that couples soft bodies with articulated rigid bodies and clothes. SoftMAC simulates soft bodies with the continuum-mechanics-based Material Point Method (MPM). We provide a novel forecast-based contact model for MPM, which effectively reduces penetration without introducing other artifacts like unnatural rebound. To couple MPM particles with deformable and non-volumetric clothes meshes, we also propose a penetration tracing algorithm that reconstructs the signed distance field in local area. Diverging from previous works, SoftMAC simulates the complete dynamics of each modality and incorporates them into a cohesive system with an explicit and differentiable coupling mechanism. The feature empowers SoftMAC to handle a broader spectrum of interactions, such as soft bodies serving as manipulators and engaging with underactuated systems. We conducted comprehensive experiments to validate the effectiveness and accuracy of the proposed differentiable pipeline in downstream robotic manipulation applications. Supplementary materials and videos are available on our project website at https://damianliumin.github.io/SoftMAC.

翻译：可微分物理仿真通过基于梯度的优化为解决先前难以处理的挑战提供了途径，从而极大提升了解决机器人相关问题的效率。为了在多样化的机器人操作场景中应用可微分仿真，一个关键挑战在于将不同材料整合到统一框架中。本文提出SoftMAC，一个将软体与铰接刚体及衣物耦合的可微分仿真框架。SoftMAC采用基于连续介质力学的物质点法（MPM）对软体进行仿真。我们为MPM提出了一种新颖的基于预测的接触模型，该模型能有效减少穿透现象，同时避免引入如非自然反弹等其他伪影。为了将MPM粒子与可变形且非体积式的衣物网格相耦合，我们还提出了一种在局部区域重建有向距离场的穿透追踪算法。与先前工作不同，SoftMAC模拟了每种模态的完整动力学，并通过显式且可微分的耦合机制将其整合为一个连贯系统。这一特性使SoftMAC能够处理更广泛的交互场景，例如软体作为操纵器与欠驱动系统进行交互。我们进行了全面的实验，以验证所提出的可微分流程在下游机器人操作应用中的有效性和准确性。补充材料与视频可在我们的项目网站 https://damianliumin.github.io/SoftMAC 获取。