18 citations · 19 across the 4 of their papers we have counts for
4 papers
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
Nicolò Botteghi, Mannes Poel, Beril Sirmacek +1
Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learni…
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach
Nicolò Botteghi, Beril Sirmacek, Khaled A. A. Mustafa +2
We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw…
The Penetration of Internet of Things in Robotics: Towards a Web of Robotic Things
Andreas Kamilaris, Nicolo Botteghi
As the Internet of Things (IoT) penetrates different domains and application areas, it has recently entered also the world of robotics. Robotics constitutes a modern and fast-evolv…
Sequential image processing methods for improving semantic video segmentation algorithms
Beril Sirmacek, Nicolò Botteghi, Santiago Sanchez Escalonilla Plaza
Recently, semantic video segmentation gained high attention especially for supporting autonomous driving systems. Deep learning methods made it possible to implement real time segm…