Document Type

Conference Paper

Publication Date

2025

DOI

10.1016/j.ifacol.2025.12.306

Publication Title

IFAC-PapersOnline

Volume

59

Issue

30

Pages

617-622

Conference Name

5th Conference on Modeling, Estimation and Control MECC 2025, October 5-8, 2025, Pittsburgh, United States

Abstract

Autonomous robotic manipulation in unstructured environments faces many challenges and is hindered by capabilities that bridge the gap between perception and acting on the world. Action plans that are centric to object motion rather than end-of-arm tooling behavior may aid this. This paper presents an autonomous action planner for a feedback linearizeable system comprised of three base motions that can be leveraged on their own or in combination to give custom motion plans. The optimization routine for the three different types of motion are presented, which are integrated into physics informed neural networks. A component of this is the autonomy that decides how to use these plans independently and in combination. This approach is experimentally demonstrated on autonomous drawer opening, door knob turning, and door knob turning with door opening.

Rights

© 2025 The Authors.

This is an open access article under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License.

Original Publication Citation

Slightam, J. E., & Beaver, L. E. (2025). Optimal manipulation motion action planner enabled by physics informed neural networks. IFAC-PapersOnLine, 59(30), 617-622. https://doi.org/10.1016/j.ifacol.2025.12.306

ORCID

0000-0002-9770-2740 (Beaver)

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