Gastone Pietro Rosati Papini — accepted AI-conference papers

2026

Nnodely - Neuralize Your Model

Gastone Pietro Rosati Papini, Alice Plebe, Mojtaba Sharifzadeh, Mattia Piazza, Sebastiano Taddei, Giovanni Scialla +5

ICRA 2026poster

Modeling and control of physical systems remain challenging for purely data-driven methods, which often lack interpretability and fail to leverage prior knowledge. Model-structured neural networks (MSNNs) embed physical laws into neural architectures; however, their design and implementation can be …

Cited by 0 code ↗Scholar ↗

2023

CACTO: Continuous Actor-Critic With Trajectory Optimization - Towards Global Optimality

Gianluigi Grandesso, Elisa Alboni, Gastone Pietro Rosati Papini, Patrick M. Wensing, Andrea Del Prete

RA-L 2023

This letter presents a novel algorithm for the continuous control of dynamical systems that combines Trajectory Optimization (TO) and Reinforcement Learning (RL) in a single framework. The motivations behind this algorithm are the two main limitations of TO and RL when applied to continuous nonlinea

Cited by 26 Source ↗Scholar ↗

2022

The Biasing of Action Selection Produces Emergent Human-Robot Interactions in Autonomous Driving

Mauro Da Lio, Riccardo Donà, Gastone Pietro Rosati Papini, Alice Plebe

RA-L 2022

This letter describes a means to produce emergent collaboration between a human driver and an artificial co-driver agent. The work exploits the hypothesis that human-human cooperation emerges from a shared understanding of the given context’s affordances and emulates the same principle: the observat

Cited by 9 Source ↗Scholar ↗