18 citations · 50 across the 11 of their papers we have counts for
12 papers
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…
Recurrent Deep Kernel Learning of Dynamical Systems
Nicolò Botteghi, Paolo Motta, Andrea Manzoni +2
Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets. However, constructing ROMs from noisy…
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…
Discovering Efficient Periodic Behaviours in Mechanical Systems via Neural Approximators
Yannik Wotte, Sven Dummer, Nicolò Botteghi +3
It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these pe…
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
Nicolò Botteghi, Mengwu Guo, Christoph Brune
This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The fram…
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 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…
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…
Low Dimensional State Representation Learning with Reward-shaped Priors
Nicolò Botteghi, Ruben Obbink, Daan Geijs +5
Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal polic…
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…