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)
Repository Citation
Slightam, Jonathon E.; Steyer, Andrew J.; Beaver, Logan E.; and Young, Carol C., "An Approach to Realize Generalized Optimal Motion Primatives Using Physics Informed Neural Networks" (2025). Mechanical & Aerospace Engineering Faculty Publications. 163.
https://digitalcommons.odu.edu/mae_fac_pubs/163