7 papers
Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture
Luis F. W. Batista, Stéphanie Aravecchia, Cédric Pradalier
Autonomous surface vessels for floating-waste removal operate under varying hydrodynamics, external disturbances, and challenging water-surface perception. We present a field-valid…
Evaluation of Polarimetric Fusion for Semantic Segmentation in Aquatic Environments
Luis F. W. Batista, Tom Bourbon, Cedric Pradalier
Accurate segmentation of floating debris on water is often compromised by surface glare and changing outdoor illumination. Polarimetric imaging offers a single-sensor route to miti…
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…
PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects
Luis Felipe Wolf Batista, Salim Khazem, Mehran Adibi +2
Plastic waste in aquatic environments poses severe risks to marine life and human health. Autonomous robots can be utilized to collect floating waste, but they require accurate obj…
Guaranteed Reach-Avoid for Black-Box Systems through Narrow Gaps via Neural Network Reachability
Long Kiu Chung, Wonsuhk Jung, Srivatsank Pullabhotla +6
In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's pe…