Differentiable Forward Kinematics for TensorFlow 2
Abstract
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
Cite
@article{arxiv.2301.09954,
title = {Differentiable Forward Kinematics for TensorFlow 2},
author = {Lukas Mölschl and Jakob J. Hollenstein and Justus Piater},
journal= {arXiv preprint arXiv:2301.09954},
year = {2023}
}