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Specifically, some efforts develop differentiable robotic physics engines motivated by the computational benefits of merging rigid body simulations with modern differentiable machine learning libraries. Here, we present a library that focuses on the ability to combine data driven methods with analytical rigid body computations. More concretely, our library \\emph{Differentiable Robot Models} implements both \\emph{differentiable} and \\emph{learnable} models of the kinematics and dynamics of robots in Pytorch. 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