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Efficient Differentiable Simulation of Articulated Bodies
[article]
2021
arXiv
pre-print
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
arXiv:2109.07719v1
fatcat:obuox7yt5rg2pfbzurwq4yyfxm