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.
To alleviate penetration, we introduce a novel method called forecast-based contact model to manage the boundary conditions for Material Point Method (MPM). Specifically, the model takes a grid-to-particle transfer to look ahead in the grid operation stage, imposes constraints on particles within the contact region, and then adjusts the grid velocity accordingly.
Forecast-based contact model requires signed distance fields (SDFs) to penalize
penetration. While the definition of SDF is straightforward
for volumetric objects, it is hard to determine the sign on
nonvolumetric meshes. To solve the problem, we propose a
penetration tracing algorithm that capitalizes on the localized
motion of particles to reconstruct the SDF within confined
zones. In this way, the contact model can be applied to both
soft-rigid and soft-cloth coupling.
We demonstrate the effectiveness of SoftMAC across six robotic manipulation tasks. For each task, we optimize an action sequence to minimize the task-specific loss using the gradient information calculated by SoftMAC. The initial, intermediate, and final trajectories are displayed from left to right.
@inproceedings{liu2023softmac,
title={SoftMAC: Differentiable Soft Body Simulation with Forecast-based Contact Model and Two-way Coupling with Articulated Rigid Bodies and Clothes},
author={Liu, Min and Yang, Gang and Luo, Siyuan and Shao, Lin},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) },
year={2024},
}