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Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots

US & West · yesterday

arXiv:2604.14678v2 Announce Type: replace Abstract: Data-driven Model Predictive Control (MPC) has lately been a core research subject in the field of control theory. The combination of an optimal control framework with deep learning paradigms opens up the possibility to accurately tracking control…

Original headline: Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots

Read the original: arXiv — Systems and Control (eess.SY)

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Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots

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Physics-Informed Neural Network Surrogates with Polynomial Chaos-Based Uncertainty Propagation for Stochastic Model Predictive Control

SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm

FinsSim: A Reality-Aligned Integrated Simulation Platform for Underwater Robot Learning