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Poster
Efficient Differentiable Simulation of Articulated Bodies
Yi-Ling Qiao · Junbang Liang · Vladlen Koltun · Ming Lin

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @ Virtual #None

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 contact solver 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.

Author Information

Yi-Ling Qiao (University of Maryland, College Park)
Junbang Liang (University of Maryland, College Park)
Vladlen Koltun (Intel Labs)
Ming Lin (UMD-CP & UNC-CH)

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