A nonlinear latent encoder plus linear latent decoder learns cross-coupled, history-dependent, and nonstationary residual dynamics for aerial manipulators, supporting online Bayesian adaptation and improved real-time MPC tracking.
Physics-inspired temporal learning of quadrotor dynamics for accurate model predictive trajectory tracking
2 Pith papers cite this work. Polarity classification is still indexing.
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Self-supervised residual learning from trajectory data forms a hybrid dynamics model that enables trajectory optimization to produce aggressive yet precisely trackable motions for quadrotors.
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Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation
A nonlinear latent encoder plus linear latent decoder learns cross-coupled, history-dependent, and nonstationary residual dynamics for aerial manipulators, supporting online Bayesian adaptation and improved real-time MPC tracking.
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Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning
Self-supervised residual learning from trajectory data forms a hybrid dynamics model that enables trajectory optimization to produce aggressive yet precisely trackable motions for quadrotors.