Abstract: In this work, we propose a high-order regularization method to solve the ill-conditioned problems in robot localization. Numerical solutions to robot localization problems are often unstable ...

Abstract: Solutions to practical optimal control problems (OCPs) may consist of control profiles that switch between control limits or assume values interior to their admissible set, either due to ...

Regularization in Action: Real-World Examples and Success Stories 📢 Exciting news! Check out our latest blog post on "Regularization in Action: Real-World Examples and Success Stories". 🌟 ...

Regularization in Action: Real-World Examples and Success Stories Introduction Regularization is a technique used in machine learning and statistical modeling to prevent overfitting and improve the ...

Single-step adversarial training (SSAT) has demonstrated the potential to achieve both efficiency and robustness. However, SSAT suffers from catastrophic overfitting (CO), a phenomenon that leads to a ...

parser.add_argument("--task", type=str, default="Isaac-Velocity-Flat-Forward-Unitree-Go2-v0") parser.add_argument("--dataset-path", type=str, default='imitation_data ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...