6 citations · 26 across the 14 of their papers we have counts for
14 papers
Multi-Object Navigation in real environments using hybrid policies
Assem Sadek, Guillaume Bono, Boris Chidlovskii +2
Navigation has been classically solved in robotics through the combination of SLAM and planning. More recently, beyond waypoint planning, problems involving significant components…
Learning whom to trust in navigation: dynamically switching between classical and neural planning
Sombit Dey, Assem Sadek, Gianluca Monaci +2
Navigation of terrestrial robots is typically addressed either with localization and mapping (SLAM) followed by classical planning on the dynamically created maps, or by machine le…
Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction
Guillaume Bono, Leonid Antsfeld, Assem Sadek +2
Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision takin…
AutoNeRF: Training Implicit Scene Representations with Autonomous Agents
Pierre Marza, Laetitia Matignon, Olivier Simonin +3
Implicit representations such as Neural Radiance Fields (NeRF) have been shown to be very effective at novel view synthesis. However, these models typically require manual and care…
Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers
Steeven Janny, Aurélien Béneteau, Madiha Nadri +3
Estimating fluid dynamics is classically done through the simulation and integration of numerical models solving the Navier-Stokes equations, which is computationally complex and t…
Multi-Object Navigation with dynamically learned neural implicit representations
Pierre Marza, Laetitia Matignon, Olivier Simonin +1
Understanding and mapping a new environment are core abilities of any autonomously navigating agent. While classical robotics usually estimates maps in a stand-alone manner with SL…
Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach
Steeven Janny, Quentin Possamai, Laurent Bako +2
The identification of a nonlinear dynamic model is an open topic in control theory, especially from sparse input-output measurements. A fundamental challenge of this problem is tha…
Learning to estimate UAV created turbulence from scene structure observed by onboard cameras
Quentin Possamaï, Steeven Janny, Madiha Nadri +2
Controlling UAV flights precisely requires a realistic dynamic model and accurate state estimates from onboard sensors like UAV, GPS and visual observations. Obtaining a precise dy…
MoCap-less Quantitative Evaluation of Ego-Pose Estimation Without Ground Truth Measurements
Quentin Possamaï, Steeven Janny, Guillaume Bono +3
The emergence of data-driven approaches for control and planning in robotics have highlighted the need for developing experimental robotic platforms for data collection. However, t…
Graph augmented Deep Reinforcement Learning in the GameRLand3D environment
Edward Beeching, Maxim Peter, Philippe Marcotte +4
We address planning and navigation in challenging 3D video games featuring maps with disconnected regions reachable by agents using special actions. In this setting, classical symb…
Godot Reinforcement Learning Agents
Edward Beeching, Jilles Debangoye, Olivier Simonin +1
We present Godot Reinforcement Learning (RL) Agents, an open-source interface for developing environments and agents in the Godot Game Engine. The Godot RL Agents interface allows…
An in-depth experimental study of sensor usage and visual reasoning of robots navigating in real environments
Assem Sadek, Guillaume Bono, Boris Chidlovskii +1
Visual navigation by mobile robots is classically tackled through SLAM plus optimal planning, and more recently through end-to-end training of policies implemented as deep networks…
Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs
Jörg Stork, Philip Wenzel, Severin Landwein +8
We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks. We implement an innovative AI-based approach to detect, classify an…
Deep KKL: Data-driven Output Prediction for Non-Linear Systems
Steeven Janny, Vincent Andrieu, Madiha Nadri +1
We address the problem of output prediction, ie. designing a model for autonomous nonlinear systems capable of forecasting their future observations. We first define a general fram…