18 citations · 50 across the 11 of their papers we have counts for
6 papers · 1 filter
Denoising Diffusion Planner: Learning Complex Paths from Low-Quality Demonstrations
Michiel Nikken, Nicolò Botteghi, Wesley Roozing +1
Denoising Diffusion Probabilistic Models (DDPMs) are powerful generative deep learning models that have been very successful at image generation, and, very recently, in path planni…
Trajectory Generation, Control, and Safety with Denoising Diffusion Probabilistic Models
Nicolò Botteghi, Federico Califano, Mannes Poel +1
We present a framework for safety-critical optimal control of physical systems based on denoising diffusion probabilistic models (DDPMs). The technology of control barrier function…
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Nicolò Botteghi, Luuk Grefte, Mannes Poel +4
Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, t…
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Nicolò Botteghi, Khaled Alaa, Mannes Poel +4
Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life. In non-structured environments and in the presence of uncertainties, su…
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…