5 papers
Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents
Angelo Moroncelli, Roberto Zanetti, Marco Maccarini +1
Reinforcement learning (RL) enables high-frequency, closed-loop control for robotic manipulation, but scaling to long-horizon tasks with sparse or imperfect rewards remains difficu…
Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics
Angelo Moroncelli, Matteo Rufolo, Gunes Cagin Aydin +2
Accurate modeling of robot dynamics is essential for model-based control, yet remains challenging under distributional shifts and real-time constraints. In this work, we formulate…
ConceptBot: Enhancing Robot's Autonomy through Task Decomposition with Large Language Models and Knowledge Graph
Alessandro Leanza, Angelo Moroncelli, Giuseppe Vizzari +3
ConceptBot is a modular robotic planning framework that combines Large Language Models and Knowledge Graphs to generate feasible and risk-aware plans despite ambiguities in natural…
GEAR: Gaze-Enabled Human-Robot Collaborative Assembly
Asad Ali Shahid, Angelo Moroncelli, Drazen Brscic +2
Recent progress in robot autonomy and safety has significantly improved human-robot interactions, enabling robots to work alongside humans on various tasks. However, complex assemb…
The Duality of Generative AI and Reinforcement Learning in Robotics: A Review
Angelo Moroncelli, Vishal Soni, Marco Forgione +3
Recently, generative AI and reinforcement learning (RL) have been redefining what is possible for AI agents that take information flows as input and produce intelligent behavior. A…