5 citations · 6 across the 10 of their papers we have counts for
10 papers
FALCON-S: Fixed-wing ground-effect Aerodynamics Simulator and Flight Control Learning Suite
Matteo El Hariry, Pedro Lima, Andrej Orsula +2
We present a modular, high-fidelity simulation framework for the development and benchmarking of flight control strategies in fixed-wing aerial robots operating near the ground. Un…
RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets
Matteo El-Hariry, Andrej Orsula, Matthieu Geist +1
The growing need for autonomous on-orbit services such as inspection, maintenance, and situational awareness calls for intelligent spacecraft capable of complex maneuvers around la…
Space Robotics Bench: Robot Learning Beyond Earth
Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez +1
The growing ambition for space exploration demands robust autonomous systems that can operate in unstructured environments under extreme extraterrestrial conditions. The adoption o…
Learning Tool-Aware Adaptive Compliant Control for Autonomous Regolith Excavation
Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez +1
Autonomous regolith excavation is a cornerstone of in-situ resource utilization for a sustained human presence beyond Earth. However, this task is fundamentally hindered by the com…
Sim2Dust: Mastering Dynamic Waypoint Tracking on Granular Media
Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez +1
Reliable autonomous navigation across the unstructured terrains of distant planetary surfaces is a critical enabler for future space exploration. However, the deployment of learnin…
RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation
Matteo El-Hariry, Antoine Richard, Ricard M. Castan +4
Autonomous robots must navigate and operate in diverse environments, from terrestrial and aquatic settings to aerial and space domains. While Reinforcement Learning (RL) has shown…
Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space
Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez +1
The ability to autonomously assemble structures is crucial for the development of future space infrastructure. However, the unpredictable conditions of space pose significant chall…
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of View
Raj Ghugare, Matthieu Geist, Glen Berseth +1
Some reinforcement learning (RL) algorithms can stitch pieces of experience to solve a task never seen before during training. This oft-sought property is one of the few ways in wh…
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
Eduardo Pignatelli, Johan Ferret, Matthieu Geist +4
The Credit Assignment Problem (CAP) refers to the longstanding challenge of Reinforcement Learning (RL) agents to associate actions with their long-term consequences. Solving the C…
DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories
Matteo El-Hariry, Antoine Richard, Vivek Muralidharan +2
This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments. Floating platforms serve…