Learning for CasADi Data-driven Models in Numerical Optimization

Tim Salzmann, Jon Arrizabalaga, Joel Andersson, Marco Pavone, Markus Ryll

Publikation: Beitrag in FachzeitschriftKonferenzartikelBegutachtung

Abstract

While real-world problems are often challenging to analyze analytically, deep learning excels in modeling complex processes from data. Existing optimization frameworks like CasADi facilitate seamless usage of solvers but face challenges when integrating learned process models into numerical optimizations. To address this gap, we present the Learning for CasADi (L4CasADi) framework, enabling the seamless integration of PyTorch-learned models with CasADi for efficient and potentially hardware-accelerated numerical optimization. The applicability of L4CasADi is demonstrated with two tutorial examples: First, we optimize a fish’s trajectory in a turbulent river for energy efficiency where the turbulent flow is represented by a PyTorch model. Second, we demonstrate how an implicit Neural Radiance Field environment representation can be easily leveraged for optimal control with L4CasADi.

OriginalspracheEnglisch
Seiten (von - bis)541-553
Seitenumfang13
FachzeitschriftProceedings of Machine Learning Research
Jahrgang242
PublikationsstatusVeröffentlicht - 2024
Veranstaltung6th Annual Learning for Dynamics and Control Conference, L4DC 2024 - Oxford, Großbritannien/Vereinigtes Königreich
Dauer: 15 Juli 202417 Juli 2024

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