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The paper treats swarm control as a learning problem. It maps Gibbs-style energy minimization onto reward maximization, trains decentralized policies once, and composes them through gating so they transfer zero-shot across mission types, swarm sizes, and platforms. The reported 0.96-1.00 success rates come from large-scale simulation, and the real-world claims rest on a limited set of physical deployments, so readers should weigh them accordingly. The most useful takeaway for ML practitioners is the reusable energy-term formulation and the compositional policy gating, which try to reduce per-task retraining in multi-agent systems.
Probabilistic Energy Modeling Unifies Versatile Swarm Control
arXiv:2610.31371v1 Announce Type: new Abstract: Robotic swarms must coordinate across diverse missions, yet existing controllers often require redesign, retuning, or retraining when objectives change. Here we show that probabilistic energy modeling provides a unifying representation for versatile swarm control, in which diverse missions emerge from compositions of shared interaction principles rather than task-specific control formulations. By exploiting the correspondence between Gibbs random field energy minimization and structured reward maximization, we encode fundamental swarm behaviors as reusable energy terms within a unified probabilistic objective. Decentralized policies trained within this representation are composed into a single controller through lightweight policy gating, without retraining. An interpretable action-attention mechanism captures non-uniform local interactions, while dimensionless normalization supports transfer across swarm scales and platforms. Demonstrated on aerial swarms, the unified policy generalizes zero-shot across six mission classes, 10-100 agents, two hardware platforms, and indoor cluttered and GPS-denied woodland environments. Large-scale simulations achieve 0.96-1.00 task success rates with 1-10 ms per-agent inference latency, while physical deployments confirm zero-shot transfer to real platforms and unstructured environments. These results establish probabilistic energy modeling as a scalable and transferable framework for decentralized swarm control.
Abstract:Robotic swarms must coordinate across diverse missions, yet existing controllers often require redesign, retuning, or retraining when objectives change. Here we show that probabilistic energy modeling provides a unifying representation for versatile swarm control, in which diverse missions emerge from compositions of shared interaction principles rather than task-specific control formulations. By exploiting the correspondence between Gibbs random field energy minimization and structured reward maximization, we encode fundamental swarm behaviors as reusable energy terms within a unified probabilistic objective. Decentralized policies trained within this representation are composed into a single controller through lightweight policy gating, without retraining. An interpretable action-attention mechanism captures non-uniform local interactions, while dimensionless normalization supports transfer across swarm scales and platforms. Demonstrated on aerial swarms, the unified policy generalizes zero-shot across six mission classes, 10-100 agents, two hardware platforms, and indoor cluttered and GPS-denied woodland environments. Large-scale simulations achieve 0.96-1.00 task success rates with 1-10 ms per-agent inference latency, while physical deployments confirm zero-shot transfer to real platforms and unstructured environments. These results establish probabilistic energy modeling as a scalable and transferable framework for decentralized swarm control.
Robotics (cs.RO); Multiagent Systems (cs.MA)
arXiv:2610.31371 [cs.RO]
(or arXiv:2610.31371v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.31371 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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