Stéphanie Aravecchia

French National Centre for Scientific Research | CNRS · UMI 2958 GeorgiaTech - CNRS

Historical charcoal production is one of the significant factors affecting today’s forest dynamics. A key challenge is to develop tools to investigate historical charcoal production over large areas, allowing a more comprehensive understanding of past impacts and history of charcoal production over a given landscape. In this study, high-resolution...

This paper revisits system identification and shows how new paradigms from machine learning can be used to improve it in the case of non-linear systems modeling from noisy and unbalanced dataset. We show that using importance sampling schemes in system identification can provide a significant performance boost in modeling, which is helpful to a pre...

In this letter 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 train a model-based RL agent in simulation to follow lake and river shores and apply it on a real Unmanned Surface Vehicle in a zero-shot setup. We demonstrate that even...

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 train a model-based RL agent in simulation to follow lake and river shores and apply it on a real Unmanned Surface Vehicle in a zero-shot setup. We demonstrate that even t...

Long-term monitoring of natural environments raises significant challenges due to the strong perceptual aliasing in trees, bushes and shrubs. This paper reports on the multi-session localization and mapping of a small lake shore using an autonomous surface vessel equipped with a 2D lidar and a camera. Our publicly available dataset includes 130 aut...

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource-use efficiency, reduction of chemical treatments, and optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solu...

Autonomous robotic weeding systems in precision farming have demonstrated their full potential to alleviate the current dependency on agrochemicals such as herbicides and pesticides, thus reducing environmental pollution and improving sustainability. However, most previous works require fast and constant‐time weed detection systems to achieve real‐...

The application of autonomous robots in agriculture is gaining more and more popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, minimization of the human effort and maximization of yield. The Flourish research project faced this challenge by developing an ada...

Autonomous robotic weeding systems in precision farming have demonstrated their full potential to alleviate the current dependency on herbicides or pesticides by introducing selective spraying or mechanical weed removal modules, thus reducing the environmental pollution and improving the sustainability. However, most previous works require fast wee...