A hardware deep photonic reservoir computer compensates UAV residual forces in confined spaces with accuracy matching or exceeding TCN/MLP baselines while training in milliseconds and inferring in nanoseconds.
Neural-fly enables rapid learning for agile flight in strong winds
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 7representative citing papers
A dual-pathway Neural-ESO uses a Lipschitz-bounded neural network for feedforward disturbance prediction and a conventional ESO for online correction, guaranteeing uniform ultimate boundedness of the closed-loop system.
Robust OSCTC framework using ESO for disturbance estimation in task space, robust CBF for safety, and sliding-window conformal prediction for online disturbance bound estimation, demonstrated on 7-DoF Franka arm with mm tracking at 1 kHz.
A unified meta-representation learned from past observations combined with state-feedback calibration enables general disturbance estimation with proven convergence.
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.
SI-OCP provides long-run coverage guarantees for the lumped effect of disturbance and learning error in adaptive dynamics without derivative measurements, enabling long-run safety when combined with safety-critical controllers.
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.
citing papers explorer
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Deep Photonic Reservoir Computer Meets UAV Control: An ultra-fast learning-based compensator for agile flight in confined space
A hardware deep photonic reservoir computer compensates UAV residual forces in confined spaces with accuracy matching or exceeding TCN/MLP baselines while training in milliseconds and inferring in nanoseconds.
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Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control
A dual-pathway Neural-ESO uses a Lipschitz-bounded neural network for feedforward disturbance prediction and a conventional ESO for online correction, guaranteeing uniform ultimate boundedness of the closed-loop system.
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Robust Operational Space Control with Conformal Disturbance Bounds for Safe Redundant Manipulation
Robust OSCTC framework using ESO for disturbance estimation in task space, robust CBF for safety, and sliding-window conformal prediction for online disturbance bound estimation, demonstrated on 7-DoF Franka arm with mm tracking at 1 kHz.
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Unified Meta-Representation and Feedback Calibration for General Disturbance Estimation
A unified meta-representation learned from past observations combined with state-feedback calibration enables general disturbance estimation with proven convergence.
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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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Staggered Integral Online Conformal Prediction for Safe Dynamics Adaptation with Multi-Step Coverage Guarantees
SI-OCP provides long-run coverage guarantees for the lumped effect of disturbance and learning error in adaptive dynamics without derivative measurements, enabling long-run safety when combined with safety-critical controllers.
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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.