6 citations · 6 across the 6 of their papers we have counts for
7 papers
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…
GPS-aided Visual Wheel Odometry
Junlin Song, Pedro J. Sanchez-Cuevas, Antoine Richard +1
This paper introduces a novel GPS-aided visual-wheel odometry (GPS-VWO) for ground robots. The state estimation algorithm tightly fuses visual, wheeled encoder and GPS measurements…
How To Train Your HERON
Antoine Richard, Stephanie Aravecchia, Thomas Schillaci +2
In this paper we apply Deep Reinforcement Learning (Deep RL) and Domain Randomization to solve a navigation task in a natural environment relying solely on a 2D laser scanner. We t…
A Study on Trees's Knots Prediction from their Bark Outer-Shape
Mejri Mohamed, Antoine Richard, Cedric Pradalier
In the industry, the value of wood-logs strongly depends on their internal structure and more specifically on the knots' distribution inside the trees. As of today, CT-scanners are…
A Survey On 3D Inner Structure Prediction from its Outer Shape
Mohamed Mejri, Antoine Richard, Cédric Pradalier
The analysis of the internal structure of trees is highly important for both forest experts, biological scientists, and the wood industry. Traditionally, CT-scanners are considered…
Semantic Nearest Neighbor Fields Monocular Edge Visual-Odometry
Xiaolong Wu, Assia Benbihi, Antoine Richard +1
Recent advances in deep learning for edge detection and segmentation opens up a new path for semantic-edge-based ego-motion estimation. In this work, we propose a robust monocular…