Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms
2026-06-01 • Robotics
RoboticsMultiagent Systems
AI summaryⓘ
The authors present PhySwarm, a new method to model and control how groups of robots behave together in complex tasks that happen in multiple steps. They use physics-based math to describe the overall crowd movement (macro level) and how each robot moves (micro level). A special neural controller helps robots decide what to do based on what they see and remember, trained to balance the group’s overall behavior and individual consistency. Their approach works on several example tasks, showing how physical processes like movement, spreading out, and changing roles shape group behavior. This method helps better understand and guide robot swarms using ideas from physics and machine learning.
robot swarmemergent behavioradvection-diffusion-reactiondensity fieldpotential fieldreinforcement learningphysics-informed neural networkmulti-phase modelingdecentralized decision-makingmicro-macro framework
Authors
Zixuan Jin, Wenzhuo Zhang, Shuxian Quan, Zirui Dong, Fangwen Ye, Yuchen Shi, Cheng Xu
Abstract
Robot swarms can exhibit coherent collective behaviors through local perception, limited communication and decentralized decision-making, yet modeling and controlling such emergence remains challenging when behaviors unfold over multiple phases. Here we introduce PhySwarm, a physics-informed micro--macro framework that represents multi-stage swarm emergence as physically constrained density-field evolution coupled to executable robot motion. At the macroscopic level, a multi-phase advection--diffusion--reaction model (Macro-ADR) describes phase-dependent swarm-density evolution through directed transport, diffusion-based spatial regulation and behavioral phase transitions. At the microscopic level, an equivalent deterministic motion model (Micro-EDM) realizes these mechanisms through potential-field advection, density-gradient compensation and rate- or event-gated phase switching. A neural-physics controller (NPC) maps local observations and temporal memory to bounded physical parameters, and is trained with a reinforcement learning--PINN objective that combines task rewards with macro-scale density residuals and micro-scale motion-consistency constraints. In several proof-of-concept swarm missions -- including trail-guided foraging, formation-reconfigurable navigation and role-adaptive search and rescue -- we demonstrate that PhySwarm can generate distinct multi-stage emergent behaviors within a unified physics-informed modeling framework. The learned density fields and physical parameters provide interpretable evidence of how advection, diffusion and reaction jointly regulate multi-stage swarm organization. These results establish a physics-informed route for learning, interpreting and controlling emergent behaviors in robot swarms.