Differentiable Forward Kinematics for TensorFlow 2

TL;DR

This paper introduces a TensorFlow 2-based library that makes forward kinematics differentiable, enabling gradient-based learning and faster computations for robotic systems, compatible with ROS-URDF.

Contribution

The authors present a novel, auto-differentiable forward kinematics library for TensorFlow 2 that supports GPU acceleration and integration with robotic frameworks.

Findings

Supports gradient computation w.r.t. joint configurations and model parameters

Achieves significant performance improvements with GPU parallelization

Enables end-to-end learning in robotic systems using differentiable kinematics

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