A self-orchestrating physics-discovering neural architecture for adaptive and fault-resilient robotic motion control via RPA and digital twins
- DOI
- 10.1038/s41598-026-57474-6
- Published
- 2026-06-22
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-57474-6,
title = {A self-orchestrating physics-discovering neural architecture for adaptive and fault-resilient robotic motion control via RPA and digital twins},
author = {Daksh Singla and G. Logeswari and K. Tamilarasi and J. Deepika Roselind},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-57474-6},
url = {https://doi.org/10.1038/s41598-026-57474-6}
}RIS
TY - JOUR TI - A self-orchestrating physics-discovering neural architecture for adaptive and fault-resilient robotic motion control via RPA and digital twins AU - Daksh Singla AU - G. Logeswari AU - K. Tamilarasi AU - J. Deepika Roselind PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-57474-6 UR - https://doi.org/10.1038/s41598-026-57474-6 ER -
APA
Singla, D., Logeswari, G., Tamilarasi, K., & Roselind, J. D. (2026). A self-orchestrating physics-discovering neural architecture for adaptive and fault-resilient robotic motion control via RPA and digital twins. Scientific Reports. https://doi.org/10.1038/s41598-026-57474-6
Source records
- crossref · retrieved 2026-09-25T16:35:11.878Z