Anthony Goeckner — accepted AI-conference papers

2024

Attrition-Aware Adaptation for Multi-Agent Patrolling

Anthony Goeckner, Xinliang Li, Ermin Wei, Qi Zhu

RA-L 2024

Multi-agent patrolling is a key problem in a variety of domains such as intrusion detection, area surveillance, and policing, which involves repeated visits by a group of agents to specified points in an environment. While the problem is well-studied, most works do not provide performance guarantees

Cited by 7 Source ↗code ↗Scholar ↗

2024

Graph Neural Network-based Multi-agent Reinforcement Learning for Resilient Distributed Coordination of Multi-Robot Systems

Anthony Goeckner, Yueyuan Sui, Nicolas Martinet, Xinliang Li, Qi Zhu

IROS 2024poster

Existing multi-agent coordination techniques are often fragile and vulnerable to anomalies such as agent attrition and communication disturbances, which are quite common in the real-world deployment of systems like field robotics. To better prepare these systems for the real world, we present a grap…

Cited by 20 Source ↗Scholar ↗