Sequential image processing methods for improving semantic video segmentation algorithms. - Beril Sirmaçek; Nicolò Botteghi; Santiago Sanchez-Escalonilla Plaza (CoRR, 2019)

Low Dimensional State Representation Learning with Reward-shaped Priors. - Nicolò Botteghi; Ruben Obbink; Daan Geijs; Mannes Poel; Beril Sirmaçek; Christoph Brune; Abeje Y. Mersha; Stefano Stramigioli (ICPR, 2020)

On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach. - Nicolò Botteghi; Beril Sirmaçek; Khaled A. A. Mustafa; Mannes Poel; Stefano Stramigioli (CoRR, 2020)

The penetration of Internet of Things in robotics: Towards a web of robotic things. - Andreas Kamilaris; Nicolò Botteghi (J. Ambient Intell. Smart Environ., 2020)

A System for Efficient Path Planning and Target Assignment for Robotic Swarms in Agriculture. - Jochem Postmes; Nicolò Botteghi; Beril Sirmaçek; Andreas Kamilaris (IOT, 2021)

Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces. - Nicolò Botteghi; Khaled Alaa; Mannes Poel; Beril Sirmaçek; Christoph Brune; Abeje Y. Mersha; Stefano Stramigioli (IROS, 2021)

Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning. - Nicolò Botteghi; Luuk Grefte; Mannes Poel; Beril Sirmaçek; Christoph Brune; Edwin Dertien; Stefano Stramigioli (RiTA, 2021)

Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics. - Nicolò Botteghi; Mannes Poel; Beril Sirmaçek; Christoph Brune (CoRR, 2021)

Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data. - Nicolò Botteghi; Mengwu Guo; Christoph Brune (CoRR, 2022)

Unsupervised Representation Learning in Deep Reinforcement Learning: A Review. - Nicolò Botteghi; Mannes Poel; Christoph Brune (CoRR, 2022)

Discovering Efficient Periodic Behaviours in Mechanical Systems via Neural Approximators. - Yannik P. Wotte; Sven Dummer; Nicolò Botteghi; Christoph Brune; Stefano Stramigioli; Federico Califano (CoRR, 2022)

Trajectory Generation, Control, and Safety with Denoising Diffusion Probabilistic Models. - Nicolò Botteghi; Federico Califano; Mannes Poel; Christoph Brune (CoRR, 2023)

CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning. - Andreas Sauter; Nicolò Botteghi; Erman Acar; Aske Plaat (AAMAS, 2024)

Parametric PDE Control with Deep Reinforcement Learning and L0 Sparse Polynomial Policies. - Nicolò Botteghi; Urban Fasel (CDC, 2024)

Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies. - Nicolò Botteghi; Urban Fasel (CoRR, 2024)

Recurrent Deep Kernel Learning of Dynamical Systems. - Nicolò Botteghi; Paolo Motta; Andrea Manzoni; Paolo Zunino; Mengwu Guo (CoRR, 2024)

Sparsifying Parametric Models with L0 Regularization. - Nicolò Botteghi; Urban Fasel (CoRR, 2024)

Invisible Servoing: a Visual Servoing Approach with Return-Conditioned Latent Diffusion. - Bishoy Gerges; Barbara Bazzana; Nicolò Botteghi; Youssef Aboudorra; Antonio Franchi (CoRR, 2024)

Denoising Diffusion Planner: Learning Complex Paths from Low-Quality Demonstrations. - Michiel Nikken; Nicolò Botteghi; Wesley Roozing; Federico Califano (CoRR, 2024)

Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems. - Florian Wolf 006; Nicolò Botteghi; Urban Fasel; Andrea Manzoni (CoRR, 2024)

HypeRL: Parameter-Informed Reinforcement Learning for Parametric PDEs. - Nicolò Botteghi; Stefania Fresca; Mengwu Guo; Andrea Manzoni (CoRR, 2025)