19 citations · 35 across the 13 of their papers we have counts for
14 papers
Fleet-To-Lab: A Transfer Learning Framework For Lunar Rover Slippage Estimation Via Model Fusion
Riccardo Viviano, Saki Omi, Andrej Orsula +1
Accurate wheel slip estimation is essential for autonomous lunar rover mobility and navigation. Machine Learning models trained on terrestrial data generalize poorly to lunar terra…
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…
Reward-Free Continual Adaptation for Resilient Space Robots
Andrej Orsula, Miguel Olivares-Mendez, Carol Martinez
Space robots operate in extreme environments where hardware degradation can critically compromise traditional control strategies. While continual reinforcement learning offers a pr…
Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks
Ricard Marsal I Castan, Aman Arora, Antoine Richard +3
Autonomous free-flying robots in orbital environments require controllers that are both versatile and resource-efficient, yet maintaining a separate, task-specific policy for each…
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…
A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics
Puze Liu, Jonas Günster, Niklas Funk +17
Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges ass…
Learning to Play Air Hockey with Model-Based Deep Reinforcement Learning
Andrej Orsula
In the context of addressing the Robot Air Hockey Challenge 2023, we investigate the applicability of model-based deep reinforcement learning to acquire a policy capable of autonom…
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…
Immersive Rover Control and Obstacle Detection based on Extended Reality and Artificial Intelligence
Sofía Coloma, Alexandre Frantz, Dave van der Meer +3
Lunar exploration has become a key focus, driving scientific and technological advances. Ongoing missions are deploying rovers to the surface of the Moon, targeting the far side an…
GraspLDM: Generative 6-DoF Grasp Synthesis using Latent Diffusion Models
Kuldeep R Barad, Andrej Orsula, Antoine Richard +3
Vision-based grasping of unknown objects in unstructured environments is a key challenge for autonomous robotic manipulation. A practical grasp synthesis system is required to gene…
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning
Andrej Orsula, Simon Bøgh, Miguel Olivares-Mendez +1
Extraterrestrial rovers with a general-purpose robotic arm have many potential applications in lunar and planetary exploration. Introducing autonomy into such systems is desirable…