Differentiable physics simulation provides an avenue for tackling 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 coupling soft bodies with articulated rigid bodies and clothes. SoftMAC simulates soft bodies with the continuum-mechanics-based Material Point Method (MPM). We provide a forecast-based contact model for MPM, which greatly reduces artifacts like penetration and 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. Based on simulators for each modality and the contact model, we develop a differentiable coupling mechanism to simulate the interactions between soft bodies and the other two types of materials. Comprehensive experiments are conducted 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，从而显著提高了机器人相关问题的求解效率。为将可微仿真应用于多样化的机器人操作场景，关键挑战在于将多种材料统一整合至同一框架中。我们提出SoftMAC——一种将软体与刚体关节及衣物耦合的可微仿真框架。SoftMAC采用基于连续介质力学的物质点法（MPM）模拟软体，并提出一种面向MPM的预测接触模型，大幅减少了穿透、非自然反弹等伪影。为将MPM粒子与可变形且非体积性的衣物网格耦合，我们还提出一种穿透追踪算法，可在局部区域重建符号距离场。基于各模态的仿真器及接触模型，我们开发了可微耦合机制以模拟软体与其他两类材料间的相互作用。通过综合实验验证了所提可微流水线在下游机器人操作应用中的有效性与准确性。补充材料及演示视频详见项目网站：https://sites.google.com/view/softmac。