activity
20222025
most citedOptimality Principles in Spacecraft Neural Guidance and Control

30 citations · 48 across the 7 of their papers we have counts for

collaborators

7 papers

eess.SY2025

Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks

Harry Holt, Sebastien Origer, Dario Izzo

Guidance & control networks (G&CNETs) provide a promising alternative to on-board guidance and control (G&C) architectures for spacecraft, offering a differentiable, end-to-end rep…

math.OC2025

High-order expansion of Neural Ordinary Differential Equations flows

Dario Izzo, Sebastien Origer, Giacomo Acciarini +1

Artificial neural networks, widely recognised for their role in machine learning, are now transforming the study of ordinary differential equations (ODEs), bridging data-driven mod…

eess.SY2024

Certifying Guidance & Control Networks: Uncertainty Propagation to an Event Manifold

Sebastien Origer, Dario Izzo, Giacomo Acciarini +4

We perform uncertainty propagation on an event manifold for Guidance & Control Networks (G&CNETs), aiming to enhance the certification tools for neural networks in this field. This…

cs.LG2024★ 2 cited

Guidance and Control Networks with Periodic Activation Functions

Sebastien Origer, Dario Izzo

Inspired by the versatility of sinusoidal representation networks (SIRENs), we present a modified Guidance & Control Networks (G&CNETs) variant using periodic activation functions…

cs.RO2023★ 30 cited

Optimality Principles in Spacecraft Neural Guidance and Control

Dario Izzo, Emmanuel Blazquez, Robin Ferede +3

Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the m…

cs.RO2023★ 2 cited

Guidance & Control Networks for Time-Optimal Quadcopter Flight

Sebastien Origer, Christophe De Wagter, Robin Ferede +2

Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance & Control Networks to approximate optimal control polic…

astro-ph.EP2022★ 14 cited

Neural representation of a time optimal, constant acceleration rendezvous

Dario Izzo, Sebastien Origer

We train neural models to represent both the optimal policy (i.e. the optimal thrust direction) and the value function (i.e. the time of flight) for a time optimal, constant accele…