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://sites.google.com/view/softmac.

翻译：可微物理仿真通过基于梯度的优化为解决以往难以处理的挑战提供了途径，极大提升了机器人相关问题的求解效率。为在不同机器人操控场景中应用可微仿真，关键挑战在于将多种材料统一整合至同一框架。本文提出SoftMAC——一个将软体与铰接刚体及衣物耦合的可微仿真框架。SoftMAC基于连续介质力学的物质点法（MPM）仿真软体。我们为MPM提出了新型预测接触模型，该模型在有效减少穿透现象的同时，不会引入类自然回弹等其他伪影。为实现MPM粒子与可变形非体积衣物网格的耦合，我们进一步提出穿透追踪算法，可在局部区域重建符号距离场。不同于以往研究，SoftMAC对每种模态的完整动力学过程进行仿真，并通过显式可微耦合机制将其整合为统一系统。该特性使SoftMAC能处理更广泛的交互场景，例如将软体用作操控器或与欠驱动系统交互。我们通过综合实验验证了所提可微流水线在下游机器人操控应用中的有效性与准确性。补充材料及视频见项目网站：https://sites.google.com/view/softmac。