6 citations · 9 across the 3 of their papers we have counts for
5 papers
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…