  Robotic systems are often complex and depend on the integration of a large
number of software components. One important component in robotic systems
provides the calculation of forward kinematics, which is required by both
motion-planning and perception related components. End-to-end learning systems
based on deep learning require passing gradients across component
boundaries.Typical software implementations of forward kinematics are not
differentiable, and thus prevent the construction of gradient-based, end-to-end
learning systems. In this paper we present a library compatible with ROS-URDF
that computes forward kinematics while simultaneously giving access to the
gradients w.r.t. joint configurations and model parameters, allowing
gradient-based learning and model identification. Our Python library is based
on Tensorflow~2 and is auto-differentiable. It supports calculating a large
number of kinematic configurations on the GPU in parallel, yielding a
considerable performance improvement compared to sequential CPU-based
calculation. https://github.com/lumoe/dlkinematics.git
