Research & Projects

Solar-Electric Water Propulsion System

As Lead Research Scientist for RL-based autonomous navigation in the ICE2THRUST project, I'm developing autonomous GNC strategies for spacecraft aimed at demonstrating the world's first In-Situ Resource Utilisation (ISRU) end-to-end process chain. This European EIC Pathfinder project converts water ice extracted in space into thrust via solar-electric water electrolysis propulsion.

For more information, visit ice2thrust.space.

Fixed-Wing Robots in Ground Effect: A High-Fidelity Aerodynamic Simulation Framework for Flight Control

FALCON-S introduces a comprehensive simulation and learning framework for autonomous control of fixed-wing Wing-in-Ground (WIG) effect vehicles. The suite addresses the unique aerodynamic challenges of near-surface flight, where ground-effect interactions create highly nonlinear and rapidly varying dynamics that classical controllers struggle to handle robustly.

RSS 2026 (under review) — Matteo El-Hariry, Pedro Lima, Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez

Autonomous Guidance, Navigation, and Control for Unmanned WIG Vehicles

As Lead Research Scientist in the development of RL-based autonomous navigation systems, I'm working on autonomous Guidance, Navigation, and Control (GNC) strategies for innovative Unmanned WIG Vehicles (UWV). This Horizon Europe project focuses on developing cutting-edge autonomous systems for next-generation aviation.

For more information, check out airshipproject.eu.

A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation

RoboRAN extends IsaacLab with a modular and scalable design, enabling unified definitions of tasks and robots, and training one policy per robot-task pair using a shared infrastructure. It uniquely supports sim-to-real transfer across diverse platforms and mediums for evaluating navigation in varied environments.

For more information, visit the RoboRAN project website.

Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories

DRIFT introduces a comprehensive deep reinforcement learning framework for controlling floating platforms in both simulated and real-world environments. These platforms serve as terrestrial analogs for microgravity conditions, enabling robust testing of space navigation systems with seamless sim-to-real transfer capabilities.

Reinforcement Learning for Underactuated Satellite Attitude Control

This research investigates the application of Deep Reinforcement Learning for satellite attitude control, addressing both nominal operations and critical underactuated scenarios. The work demonstrates robust control policies capable of handling actuator failures for nanosatellites.