This paper introduces Parallel ARC (P-ARC), a parallel version of Adaptive Robot Coordination approach for multi-robot motion planning, and evaluates its performance using a set of scaling scenarios w…

Proposed an asynchronous block coordinate descent algorithm for distributed trajectory estimation in robotics, reducing communications by up to 96.9% and achieving exponential convergence.

Restructuring the communication topology among robots provides significantly greater performance gains in multi-robot coordination than simply increasing the size of the onboard AI models, given fixed…

This paper provides a systematic survey of ROS 2 middleware and identifies architectural limits through three dimensions: Space, Time, and State.

The paper proposes a novel attack paradigm demonstrating how compromising a single robot in an LLM-controlled multi-robot system can rapidly propagate malicious intent to cause coordinated unsafe acti…

The paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework that enables stable, scalable consensus control for large swarms of quadcopters using only local neighbo…

This survey synthesizes the state-of-the-art in AI-IoT-Robotics integration, proposing a modular architecture and highlighting hybrid SLM-LLM systems as the path toward next-generation Connected Robot…

This paper investigates the scaling behavior of homogeneous LLM-driven Multi-Agent Systems (MAS) and finds that performance exhibits diminishing returns due to coordination overhead, rather than scali…

The paper introduces Graph-as-Policy (GaP), a multi-agent coding harness for Variational Automation tasks that generates directed computation graphs and improves success rates and throughput through i…

The paper proposes Multi-Agent Computer Use (MACU) systems, which significantly improve performance on complex, long-horizon tasks by enabling parallel execution and dynamic task decomposition compare…

This paper explores tools for control engineers to design socio-technical systems in a more principled and ethical manner, using feedback optimization, control of Markov decision processes, and model…

This paper systematically studies the potential of prompt optimization in multi-agent systems (MAS) across various setups, revealing significant gains but also open challenges.