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

arXiv:2604.14678 · eess.SY, cs.RO, cs.SY · Submitted 2026-04-16 · Read on arXiv

eess.SY, cs.RO, cs.SY

Submitted: 2026-04-16

Updated: 2026-09-19

Code: https://github.com/johanneskbl/jsk_aerial_robot

Terminology

• Regularization — 33× in this paper · explained in 3 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Loss Function — 7× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Data-driven Model Predictive Control — 2× in this paper
• Model Predictive Control (MPC) — 2× in this paper · explained in 5 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Gaussian Processes — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Sequential Quadratic Programming — 1× in this paper

Sources

• Learning End-to-End Control for Omnidirectional Aerial Motion on Overactuated Tilt-rotor Quadrotors
• Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
• L4acados: Learning-based models for acados, applied to Gaussian process-based predictive control
• Decoupled Weight Decay Regularization
• Gaussian Error Linear Units (GELUs)

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