Efficient Differentiable Simulation of Articulated Bodies

• URL: http://arxiv.org/abs/2109.07719v1
• Date: Thu, 16 Sep 2021 04:48:13 GMT
• Title: Efficient Differentiable Simulation of Articulated Bodies
• Authors: Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, and Ming C. Lin
• Abstract summary: We present a method for efficient differentiable simulation of articulated bodies. This enables integration of articulated body dynamics into deep learning frameworks. We show that reinforcement learning with articulated systems can be accelerated using gradients provided by our method.
• Score: 89.64118042429287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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