output
20072026
most citedQuantum key distribution over 25 km with an all-fiber continuous-variable system

544 citations

35 papers

cs.RO2026

Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture

Luis F. W. Batista, Stéphanie Aravecchia, Cédric Pradalier

Autonomous surface vessels for floating-waste removal operate under varying hydrodynamics, external disturbances, and challenging water-surface perception. We present a field-valid…

eess.SY2026

Validation methodology on real data of reversible Kalman Filter for state estimation with Manifold

Svyatoslav Covanov, Cedric Pradalier

This work extends a previous study that introduced an algorithm for state estimation on manifolds within the framework of the Kalman filter. Its objective is to address the limitat…

cs.CV2025

Evaluation of Polarimetric Fusion for Semantic Segmentation in Aquatic Environments

Luis F. W. Batista, Tom Bourbon, Cedric Pradalier

Accurate segmentation of floating debris on water is often compromised by surface glare and changing outdoor illumination. Polarimetric imaging offers a single-sensor route to miti…

eess.SY2025

Reversible Kalman Filter for state estimation with Manifold

Svyatoslav Covanov, Cedric Pradalier

This work introduces an algorithm for state estimation on manifolds within the framework of the Kalman filter. Its primary objective is to provide a methodology enabling the evalua…

cond-mat.mtrl-sci2024★ 2 cited

On the importance of Ni-Au-Ga interdiffusion in the formation of a Ni-Au / p-GaN ohmic contact

Jules Duraz, Hassen Souissi, Maksym Gromovyi +12

The Ni-Au-Ga interdiffusion mechanisms taking place during rapid thermal annealing (RTA) under oxygen atmosphere of a Ni-Au/p-GaN contact are investigated by high-resolution transm…

cs.RO2024

A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation

Luis F W Batista, Junghwan Ro, Antoine Richard +3

Despite the increasing adoption of Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), there still remain challenges limiting real-world deployment. In this p…