6 citations · 20 across the 8 of their papers we have counts for
10 papers
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…
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…
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…
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
Pierre Marza, Laetitia Matignon, Olivier Simonin +1
In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently re…
Learning to plan with uncertain topological maps
Edward Beeching, Jilles Dibangoye, Olivier Simonin +1
We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven…
EgoMap: Projective mapping and structured egocentric memory for Deep RL
Edward Beeching, Christian Wolf, Jilles Dibangoye +1
Tasks involving localization, memorization and planning in partially observable 3D environments are an ongoing challenge in Deep Reinforcement Learning. We present EgoMap, a spatia…
Towards S-NAMO: Socially-aware Navigation Among Movable Obstacles
Benoit Renault, Jacques Saraydaryan, Olivier Simonin
In this paper, we present an in-depth analysis of Navigation Among Movable Obstacles (NAMO) literature, notably highlighting that social acceptability remains an unadressed problem…
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer
Edward Beeching, Christian Wolf, Jilles Dibangoye +1
An important goal of research in Deep Reinforcement Learning in mobile robotics is to train agents capable of solving complex tasks, which require a high level of scene understandi…
Multi-UAV Visual Coverage of Partially Known 3D Surfaces: Voronoi-based Initialization to Improve Local Optimizers
Alessandro Renzaglia, Jilles Dibangoye, Vincent Le Doze +1
In this paper we study the problem of steering a team of Unmanned Aerial Vehicles (UAVs) toward a static configuration which maximizes the visibility of a 3D environment. The UAVs…