5 papers · 1 filter
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…
Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests
Luis F. W. Batista, Stéphanie Aravecchia, Seth Hutchinson +1
Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external…
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…
A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation
Luis F W Batista, Junghwan Ro, Antoine Richard +3
Despite the increasing adoption of Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), there still remain challenges limiting real-world deployment. In this p…
Trust and Acceptance of Multi-Robot Systems "in the Wild". A Roadmap exemplified within the EU-Project BugWright2
Pete Schroepfer, Nathalie Schauffel, Jan Gründling +3
This paper outlines a roadmap to effectively leverage shared mental models in multi-robot, multi-stakeholder scenarios, drawing on experiences from the BugWright2 project. The disc…