We present a method for efficient differentiable simulation of articulated bodies. This enables integration of articulated body dynamics into deep learning frameworks, and gradient-based optimization of neural networks that operate on articulated bodies. We derive the gradients of the forward dynamics using spatial algebra and the adjoint method. Our approach is an order of magnitude faster than autodiff tools. By only saving the initial states throughout the simulation process, our method reduces memory requirements by two orders of magnitude. We demonstrate the utility of efficient differentiable dynamics for articulated bodies in a variety of applications. We show that reinforcement learning with articulated systems can be accelerated using gradients provided by our method. In applications to control and inverse problems, gradient-based optimization enabled by our work accelerates convergence by more than an order of magnitude.
翻译:我们提出了一个对分解体进行有效不同模拟的方法。 这样可以将分解体体动态整合到深层学习框架中, 并对在分解体上运行的神经网络进行梯度优化。 我们使用空间代数和连接法来计算前方动态的梯度。 我们的方法比自动变换工具要快得多。 我们的方法仅仅在整个模拟过程中保存最初的状态,就能将内存要求减少两个级。 我们展示了在多种应用中,对分解体的有效分解体动态的效用。 我们展示了利用我们的方法提供的梯度来加速用分解系统进行强化学习。 在应用来控制和反向的问题中,我们的工作所促成的梯度优化将加速速度超过一个级的趋同。