232 citations · 381 across the 25 of their papers we have counts for
6 papers · 1 filter
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments
Daniel Dugas, Juan Nieto, Roland Siegwart +1
Robot navigation is a task where reinforcement learning approaches are still unable to compete with traditional path planning. State-of-the-art methods differ in small ways, and do…
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
Le Chen, Yunke Ao, Florian Tschopp +5
Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of…
With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance
Álvaro Serra-Gómez, Bruno Brito, Hai Zhu +2
Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coo…
Go Fetch: Mobile Manipulation in Unstructured Environments
Kenneth Blomqvist, Michel Breyer, Andrei Cramariuc +7
With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved…
Accurate Mapping and Planning for Autonomous Racing
Leiv Andresen, Adrian Brandemuehl, Alex Hönger +11
This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student G…
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
Julien Kindle, Fadri Furrer, Tonci Novkovic +3
Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a re…