An Approach to Realize Generalized Optimal Motion Primatives Using Physics Informed Neural Networks

Document Type

Article

Publication Date

2025

DOI

10.1115/1.4066627

Publication Title

ASME Letters in Dynamic Systems and Control

Volume

5

Issue

2

Pages

021001 (1-6)

Abstract

Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. This work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

Rights

© 2024 ASME.

This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Governments contributions.

Data Availability

Article states: "The datasets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request."

Original Publication Citation

Slightam, J. E., Steyer, A. J., Beaver, L. E., & Young, C. C. (2025). An approach to realize generalized optimal motion primitives using physics informed neural networks. ASME Letters in Dynamic Systems and Control, 5(2), 1-6, Article 021001. https://doi.org/10.1115/1.4066627

ORCID

0000-0002-9770-2740 (Beaver)

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