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Alexander Davydov

4 accepted papers

2026

First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling

Alexander Davydov, Franck Djeumou, Marcus Greiff, Makoto Suminaka, Michael Thompson, John Subosits +1
ICRA 2026poster

Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. However, when deployed on unstable systems, online adaptation may not be fast enough to ensure reliable simultaneous learning and control. For example, a controller o…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling

Alexander Davydov, Franck Djeumou, Marcus Greiff, Makoto Suminaka, Michael Thompson, John K. Subosits +1
RA-L 2025

Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. However, when deployed on unstable systems, online adaptation may not be fast enough to ensure reliable simultaneous learning and control. For example, a controller o

Cited by 8Source ↗Scholar ↗
2024

Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees

Sean Jaffe, Alexander Davydov, Deniz Lapsekili, Ambuj Singh, Francesco Bullo
NeurIPS 2024poster

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), the first neural network-based dynamical system with global contractivity guarantees…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2021

Robust Implicit Networks via Non-Euclidean Contractions

Saber Jafarpour, Alexander Davydov, Anton Proskurnikov, Francesco Bullo
NeurIPS 2021poster

Implicit neural networks, a.k.a., deep equilibrium networks, are a class of implicit-depth learning models where function evaluation is performed by solving a fixed point equation. They generalize classic feedforward models and are equivalent to infinite-depth weight-tied feedforward networks. While…

Cited by 45Source ↗PDF ↗code ↗Scholar ↗