4 papers
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…
Impact-Robust Posture Optimization for Aerial Manipulation
Amr Afifi, Ahmad Gazar, Javier Alonso-Mora +2
We present a novel method for optimizing the posture of kinematically redundant torque-controlled robots to improve robustness during impacts. A rigid impact model is used as the b…
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…
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…