IEEE

On the Regret of Model Predictive Control With Imperfect Inputs

Abstract: Implementing model predictive control (MPC) in practice faces many subtle but prevalent problems, including modeling errors, solver errors, and actuator faults. In essence, the real control ...

Even weak ocean models can provide valuable information for environmental forecasts, study shows

Oxygen depletion in the western Baltic Sea is not uncommon. Oxygen-poor conditions regularly occur in deeper waters, placing ...

How a machine learning engineer built a churn prediction model that transformed a subscription platform’s retention strategy

A subscription technology platform with over 100,000 users was losing customers each month despite having access to ...
GitHub

StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing

[2025/12/25] We've released RoboCasa evaluation support, which was trained without pretraining and reached SOTA performance. Check out more details in examples/Robocasa_tabletop. [2025/12/15] ...
IEEE

Practical Nonlinear Model Predictive Control With Kolmogorov–Arnold Network Models

Abstract: This paper presents a novel approach to practical nonlinear model predictive control (PNMPC) using Kolmogorov–Arnold networks (KANs) as prediction models. KANs are based on the ...