# Differentiable Forward Kinematics for TensorFlow 2

arXiv: https://arxiv.org/abs/2301.09954 · HTML: https://mlsanity.com/paper/2301.09954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2023.

## Built on by (most cited first, 1 of 1)

- [MimicIK: Real-Time Generative Inverse Kinematics from Teleoperation with FK Consistency](https://mlsanity.com/paper/2606.15148/) (2026)

## Abstract (arXiv)

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