Research authors: Yi-Ling Qiao (University of Maryland, College Park), Junbang Liang (University of Maryland, College Park), Vladlen Koltun (Intel Labs), Ming C. Lin (University of Maryland, College Park)
Abstract: Researchers developed a scalable framework for differentiable physics that can support a large number of objects and their interactions. To accommodate objects with arbitrary geometry and topology, they adopt meshes as a representation and leverage the sparsity of contacts for scalable differentiable collision handling. Collisions are resolved in localized regions to minimize the number of optimization variables even when the number of simulated objects is high. They further accelerate implicit differentiation of optimization with nonlinear constraints. Experiments demonstrate that the presented framework requires up to two orders of magnitude less memory and computation in comparison to recent particle-based methods. They further validate the approach on inverse problems and control scenarios, where it outperforms derivative-free and model-free baselines by at least an order of magnitude.