NODEIK: Solving Inverse Kinematics with Neural Ordinary Differential Equations for Path Planning

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

This paper proposes a novel inverse kinematics (IK) solver of articulated robotic systems for path planning. IK is a traditional but essential problem for robot manipulation. Recently, data-driven methods have been proposed to quickly solve IK for path planning. These machine learning-based models can handle a large amount of IK requests at once by leveraging the GPU. However, such methods suffer from reduced accuracy and considerable training time. We propose an IK solver that improves accuracy and memory efficiency with continuous normalizing flows by utilizing the continuous hidden dynamics of a Neural ODE network. The performance is compared using multiple robots, and our method is shown to be highly performant on complex (including dual end effector) manipulators.

Identifier

85146578700 (Scopus)

ISBN

[9788993215243]

Publication Title

International Conference on Control Automation and Systems

External Full Text Location

https://doi.org/10.23919/ICCAS55662.2022.10003852

ISSN

15987833

First Page

944

Last Page

949

Volume

2022-November

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