33 citations · 81 across the 20 of their papers we have counts for
26 papers
Proof-Carrying Evolution: Black-Box Search, White-Box Flight
Amit Jain, Sergio Sanchez-Hurtado, Victor Rodriguez-Fernandez +1
We study whether a language model can help design controllers whose behavior can be formally checked. In proof-carrying evolution (PCE), a search proposes controllers together with…
Adversarial Reinforcement Learning for Robust Planetary Landing
Miguel Leiva-Vélez, Nicolas Gaston-Rozado, Andrea Scorsoglio +3
Deep reinforcement learning (DRL) policies for planetary landing and other aerospace guidance, navigation, and control tasks are commonly trained under randomized disturbances to i…
VISTA: An Attention-Based Multi-Agent Reinforcement Learning Architecture for Space Situational Awareness Sensor Tasking
Miguel Leiva-Vélez, Adalberto Claudio Quiros, Nicolas Gaston Rozado +2
The rapid growth of resident space objects is increasing the complexity of space situational awareness sensor tasking, challenging classical optimization methods as they allocate f…
DreamSat-Bench: Development and Initial Testing of a Testbed for AI-Based Pose Estimation from 3D Reconstruction
Alex Posadas-Nava, August Berne, Giovanni Lavezzi +8
This paper presents the development and initial testing of DreamSat-Bench, a modular rendezvous and proximity operation testbed designed to benchmark AI-based relative navigation t…
DreamSat-Pose: Spacecraft Pose Estimation from Single-View 3D Reconstructions and Learned 2D-3D Feature Matching
Josiane Uwumukiza, Jocelyn Zhao, Giovanni Lavezzi +5
6-DoF pose estimation is a critical task in autonomous rendezvous and proximity operations. In the case of an unknown target, this task becomes challenging as it shall be paired wi…
Graph Neural Networks Trained on Null Signal for Angle Reconstruction in X-ray Polarimetry
Vittorio Latorre, Victor Rodriguez-Fernandez, Alessandro Di Marco +3
Scientific real detectors often produce sparse, irregular data defined on non-Euclidean domains, where conventional convolutional neural networks (CNNs) impose geometric biases tha…
GUIDE: Guided Updates for In-context Decision Evolution in LLM-Driven Spacecraft Operations
Alejandro Carrasco, Mariko Storey-Matsutani, Victor Rodriguez-Fernandez +1
Large language models (LLMs) have been proposed as supervisory agents for spacecraft operations, but existing approaches rely on static prompting and do not improve across repeated…
Can LLMs Do Rocket Science? Exploring the Limits of Complex Reasoning with GTOC 12
Iñaki del Campo, Pablo Cuervo, Victor Rodriguez-Fernandez +2
Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation and general reasoning, yet their capacity for autonomous multi-stage planning in high-dimen…
Multi-Phase Spacecraft Trajectory Optimization via Transformer-Based Reinforcement Learning
Amit Jain, Victor Rodriguez-Fernandez, Richard Linares
Autonomous spacecraft control for mission phases such as launch, ascent, stage separation, and orbit insertion remains a critical challenge due to the need for adaptive policies th…
DreamSat-2.0: Towards a General Single-View Asteroid 3D Reconstruction
Santiago Diaz, Xinghui Hu, Josiane Uwumukiza +3
To enhance asteroid exploration and autonomous spacecraft navigation, we introduce DreamSat-2.0, a pipeline that benchmarks three state-of-the-art 3D reconstruction models-Hunyuan-…
Large Language Models as Autonomous Spacecraft Operators in Kerbal Space Program
Alejandro Carrasco, Victor Rodriguez-Fernandez, Richard Linares
Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompts. We intend to apply these…
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics
Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, Javier Huertas-Tato +2
The present study explores the interpretability of latent spaces produced by time series foundation models, focusing on their potential for visual analysis tasks. Specifically, we…
Fine-Tuned Language Models as Space Systems Controllers
Enrico M. Zucchelli, Di Wu, Julia Briden +3
Large language models (LLMs), or foundation models (FMs), are pretrained transformers that coherently complete sentences auto-regressively. In this paper, we show that LLMs can con…
Visual Language Models as Operator Agents in the Space Domain
Alejandro Carrasco, Marco Nedungadi, Enrico M. Zucchelli +3
This paper explores the application of Vision-Language Models (VLMs) as operator agents in the space domain, focusing on both software and hardware operational paradigms. Building…
DreamSat: Towards a General 3D Model for Novel View Synthesis of Space Objects
Nidhi Mathihalli, Audrey Wei, Giovanni Lavezzi +4
Novel view synthesis (NVS) enables to generate new images of a scene or convert a set of 2D images into a comprehensive 3D model. In the context of Space Domain Awareness, since sp…
Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space Program
Alejandro Carrasco, Victor Rodriguez-Fernandez, Richard Linares
Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompt. This study explores the u…
Exploring Scalability in Large-Scale Time Series in DeepVATS framework
Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, David Camacho
Visual analytics is essential for studying large time series due to its ability to reveal trends, anomalies, and insights. DeepVATS is a tool that merges Deep Learning (Deep) with…
Generative Design of Periodic Orbits in the Restricted Three-Body Problem
Alvaro Francisco Gil, Walther Litteri, Victor Rodriguez-Fernandez +2
The Three-Body Problem has fascinated scientists for centuries and it has been crucial in the design of modern space missions. Recent developments in Generative Artificial Intellig…
Enhancing Solar Driver Forecasting with Multivariate Transformers
Sergio Sanchez-Hurtado, Victor Rodriguez-Fernandez, Julia Briden +2
In this work, we develop a comprehensive framework for F10.7, S10.7, M10.7, and Y10.7 solar driver forecasting with a time series Transformer (PatchTST). To ensure an equal represe…
Language Models are Spacecraft Operators
Victor Rodriguez-Fernandez, Alejandro Carrasco, Jason Cheng +3
Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompts. We intend to apply these…
A revision on Multi-Criteria Decision Making methods for Multi-UAV Mission Planning Support
Cristian Ramirez-Atencia, Victor Rodriguez-Fernandez, David Camacho
Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main prob…
Towards a Machine Learning-Based Approach to Predict Space Object Density Distributions
Victor Rodriguez-Fernandez, Sumiyajav Sarangerel, Peng Mun Siew +3
With the rapid increase in the number of Anthropogenic Space Objects (ASOs), Low Earth Orbit (LEO) is facing significant congestion, thereby posing challenges to space operators an…
Self-supervised Machine Learning Based Approach to Orbit Modelling Applied to Space Traffic Management
Emma Stevenson, Victor Rodriguez-Fernandez, Hodei Urrutxua +2
This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model. Taking…
Transformer-based Atmospheric Density Forecasting
Julia Briden, Peng Mun Siew, Victor Rodriguez-Fernandez +1
As the peak of the solar cycle approaches in 2025 and the ability of a single geomagnetic storm to significantly alter the orbit of Resident Space Objects (RSOs), techniques for at…