Publications (82)
Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning
Qingbiao Li, Weizhe Lin, Zhe Liu +1
The domains of transport and logistics are increasingly relying on autonomous mobile robots for the handling and distribution of passengers or resources. At large system scales, fi…
Heterogeneous Multi-Robot Reinforcement Learning
Matteo Bettini, Ajay Shankar, Amanda Prorok
Cooperative multi-robot tasks can benefit from heterogeneity in the robots' physical and behavioral traits. In spite of this, traditional Multi-Agent Reinforcement Learning (MARL)…
ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination
Michael Amir, Guang Yang, Zhan Gao +3
Constraint-based optimization is a cornerstone of robotics, enabling the design of controllers that reliably encode task and safety requirements such as collision avoidance or form…
A Fleet of Miniature Cars for Experiments in Cooperative Driving
Nicholas Hyldmar, Yijun He, Amanda Prorok
We introduce a unique experimental testbed that consists of a fleet of 16 miniature Ackermann-steering vehicles. We are motivated by a lack of available low-cost platforms to suppo…
On the Trade-Off between Stability and Representational Capacity in Graph Neural Networks
Zhan Gao, Amanda Prorok, Elvin Isufi
Analyzing the stability of graph neural networks (GNNs) under topological perturbations is key to understanding their transferability and the role of each architecture component. H…
Generalized Intention Modeling in Multi-Agent Reinforcement Learning
Mateusz Odrowaz-Sypniewski, Jasmine Bayrooti, Ajay Shankar +1
Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent model…