Agent-Based Emulation for Deploying Robot Swarm Behaviors
Ricardo Vega, Kevin Zhu, Connor Mattson, Daniel S. Brown, Cameron, Nowzari

TL;DR
This paper presents a bottom-up agent-based emulation approach for deploying robot swarm behaviors, integrating real-world experiments with lightweight simulations to facilitate the discovery and reproduction of emergent behaviors in simple robots.
Contribution
It introduces an embodied agent-based modeling and simulation method that balances physical experiments with lightweight simulations, enabling efficient hypothesis testing and behavior discovery in robot swarms.
Findings
Successfully emulated two known swarm behaviors
Discovered a new emergent behavior accidentally
Demonstrated the effectiveness of the RSRS process
Abstract
Despite significant research, robotic swarms have yet to be useful in solving real-world problems, largely due to the difficulty of creating and controlling swarming behaviors in multi-agent systems. Traditional top-down approaches in which a desired emergent behavior is produced often require complex, resource-heavy robots, limiting their practicality. This paper introduces a bottom-up approach by employing an Embodied Agent-Based Modeling and Simulation approach, emphasizing the use of simple robots and identifying conditions that naturally lead to self-organized collective behaviors. Using the Reality-to-Simulation-to-Reality for Swarms (RSRS) process, we tightly integrate real-world experiments with simulations to reproduce known swarm behaviors as well as discovering a novel emergent behavior without aiming to eliminate or even reduce the sim2real gap. This paper presents the…
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Taxonomy
TopicsModular Robots and Swarm Intelligence · Multi-Agent Systems and Negotiation
