We present a novel, fast differentiable simulator for soft-body learning and control applications. Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods. Simulators using explicit time-stepping scheme require tiny time steps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method to solve the expensive linearized dynamics. Inspired by Projective Dynamics (PD), we present DiffPD, an efficient differentiable soft-body simulator with implicit time integration. The key idea in DiffPD is to speed up backpropagation by exploiting the prefactorized Cholesky decomposition in PD to achieve a super-linear convergence rate. To handle contacts, DiffPD solves contact forces by analyzing a linear complementarity problem (LCP) and its gradients. With the assumption that contacts occur on a small number of nodes, we develop an efficient method for gradient computation by exploring the low-rank structure in the linearized dynamics. We evaluate the performance of DiffPD and observe a speedup of 4-19 times compared to the standard Newton's method in various applications including system identification, inverse design problems, trajectory optimization, and closed-loop control.
翻译:我们为软体学习和控制应用程序提供了一个新颖的、快速不同的模拟器。 现有的不同的软体模拟器可以根据其时间整合方法分为两类。 使用明确时间步制的模拟器需要微小的时间步骤以避免梯度计算中的数字不稳定性, 使用隐含时间整合的模拟器通常通过使用连接方法计算梯度, 以解决昂贵的线性动态。 在投影动态( PPD) 的启发下, 我们提出DiffPD, 一个高效的、可区分的软体模拟器, 并隐含时间整合。 DiffPD 的关键想法是利用PD 中预设的Choolesky变形加速反射, 以达到超级线性趋同率。 要处理联系人, DiffPD 解动器通过分析线性互补问题( LCP) 及其梯度来解析梯度。 假设接触发生在少数节点, 我们开发一种高效的梯度计算方法, 探索线性动态中低级结构。 我们评估DiffPD的性调整过程的性变速性, 并观察了新- 19 标准级设计时间的轨迹的轨迹的系统, 的升级化方法, 比较了新- 的升级到标准的轨迹 。