SoftMAC：基于预测接触模型的可微软体仿真及与铰接刚体和衣物的双向耦合
SoftMAC: Differentiable Soft Body Simulation with Forecast-based Contact Model and Two-way Coupling with Articulated Rigid Bodies and Clothes
AI总结：
SoftMAC是一个可微仿真框架，通过基于预测的MPM接触模型和穿透追踪算法，实现软体、铰接刚体与衣物的双向耦合，有效支持更广泛的机器人操作交互。
摘要：
可微物理仿真为通过基于梯度的优化解决以往棘手的挑战提供了途径，从而极大提高了解决机器人相关问题的效率。要在多样的机器人操作场景中应用可微仿真，一个关键挑战是在统一框架中整合各种材料。我们提出了SoftMAC，一个将软体与铰接刚体和衣物耦合的可微仿真框架。SoftMAC使用基于连续介质力学的物质点方法（MPM）模拟软体。我们为MPM提供了一种新颖的基于预测的接触模型，该模型有效减少了穿透，且未引入不自然反弹等伪影。为了将MPM粒子与可变形且非体积的衣物网格耦合，我们还提出了一种穿透追踪算法，用于重建局部区域的符号距离场。与以往工作不同，SoftMAC模拟了每种模态的完整动力学，并通过显式且可微的耦合机制将它们整合为一个内聚系统。此特性使SoftMAC能够处理更广泛的交互，例如软体作为操作器并与欠驱动系统交互。我们进行了全面实验，验证了所提出的可微流程在下游机器人操作应用中的有效性和准确性。补充材料和视频可在我们的项目网站获取。
英文摘要：
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://minliu01.github.io/SoftMAC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
