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
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A unified meta-representation learned from past observations combined with state-feedback calibration enables general disturbance estimation with proven convergence.
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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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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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.