activity
20242026
collaborators

7 papers

cs.RO2026

Federated Multi-Agent Mapping for Planetary Exploration

Tiberiu-Ioan Szatmari, Abhishek Cauligi

Multi-agent robotic exploration stands to play an important role in space exploration as the next generation of robotic systems ventures to far-flung environments. A key challenge…

stat.ML2025

Learning Decentralized Routing Policies via Graph Attention-based Multi-Agent Reinforcement Learning in Lunar Delay-Tolerant Networks

Federico Lozano-Cuadra, Beatriz Soret, Marc Sanchez Net +2

We present a fully decentralized routing framework for multi-robot exploration missions operating under the constraints of a Lunar Delay-Tolerant Network (LDTN). In this setting, a…

math.OC2025

Tight Constraint Prediction of Six-Degree-of-Freedom Transformer-based Powered Descent Guidance

Julia Briden, Trey Gurga, Breanna Johnson +2

This work introduces Transformer-based Successive Convexification (T-SCvx), an extension of Transformer-based Powered Descent Guidance (T-PDG), generalizable for efficient six-degr…

cs.RO2025

Diffusion Policies for Generative Modeling of Spacecraft Trajectories

Julia Briden, Breanna Johnson, Richard Linares +1

Machine learning has demonstrated remarkable promise for solving the trajectory generation problem and in paving the way for online use of trajectory optimization for resource-cons…

physics.space-ph2024

Autonomy in the Real-World: Autonomous Trajectory Planning for Asteroid Reconnaissance via Stochastic Optimization

Kazuya Echigo, Abhishek Cauligi, Saptarshi Bandyopadhyay +4

This paper presents the development and evaluation of an optimization-based autonomous trajectory planning algorithm for the asteroid reconnaissance phase of a deep-space explorati…

cs.RO2024

Constraint-Informed Learning for Warm Starting Trajectory Optimization

Julia Briden, Changrak Choi, Kyongsik Yun +2

Future spacecraft and surface robotic missions require increasingly capable autonomy stacks for exploring challenging and unstructured domains, and trajectory optimization will be…