1 citations · 1 across the 6 of their papers we have counts for
7 papers
Weakly Supervised Discovery of Motion Styles for Compositional Robot Control
Heedo Woo, Antonio Marino, Eduardo Sebastián +1
When a robot possesses more degrees of freedom than a task requires, multiple trajectories can achieve the same objective using distinct motion styles. Compositional robot control…
A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation
Antonio Marino, Claudio Pacchierotti, Paolo Robuffo Giordano
Dynamic average estimation is a critical problem in multi-agent systems, enabling agents to collaboratively estimate time-varying signals using only local information exchange. Tra…
Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning
Antonio Marino, Esteban Restrepo, Soon-jo Chung +2
Multi-robot systems must simultaneously optimize competing objectives while maintaining coordinated behavior. Existing multi-agent reinforcement learning approaches often rely on f…
Stable Transformer-Actor-Critic Model Predictive Control: A Contraction Analysis Approach
Antonio Marino, Valerio Modugno, Marco Cognetti
Actor-Critic Model Predictive Control (MPC) effectively addresses complex, non-convex control problems, but guaranteeing the closed-loop stability of sequence-based learning models…
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements
Antonio Marino, Esteban Restrepo, Claudio Pacchierotti +1
This paper addresses the challenge of allocating heterogeneous resources among multiple agents in a decentralized manner. Our proposed method, Liquid-Graph-Time Clustering-IPPO, bu…
Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data
Antonio Marino, Claudio Pacchierotti, Paolo Robuffo Giordano
This paper introduces an end-to-end trajectory planning algorithm tailored for multi-UAV systems that generates collision-free trajectories in environments populated with both stat…
Liquid-Graph Time-Constant Network for Multi-Agent Systems Control
Antonio Marino, Claudio Pacchierotti, Paolo Robuffo Giordano
In this paper, we propose the Liquid-Graph Time-constant (LGTC) network, a continuous graph neural network(GNN) model for control of multi-agent systems based on therecent Liquid T…