Probabilistic Energy Modeling for Versatile Swarm Control
Overview
This research proposes probabilistic energy modeling as a scalable framework for decentralized swarm control, enabling zero-shot generalization across six mission classes, 10-100 agents, two hardware platforms, and environments with GPS denial. The approach uses energy terms derived from Gibbs random field minimization and structured reward maximization, allowing policies to transfer without retraining.
From arXiv Robotics — research abstracts
Event evidence and corrections
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Reported quantity · task success rates: 0.96 other · Basis not reported
“-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”
Exact source · revision 1Source owner not reported
Reported quantity · inference latency: 0.96 other · Basis not reported · Differing source assertions
“-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”
Exact source · revision 1Source owner not reported
Reported quantity · inference latency: 10 other · Basis not reported · Differing source assertions
“-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”
Exact source · revision 1Source owner not reported
Reported quantity · mission classes: 6 other · Basis not reported
“-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”
Exact source · revision 1Source owner not reported
Reported quantity · number of agents: 10 other · Basis not reported
“-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”
Exact source · revision 1Source owner not reported
Reported quantity · hardware platforms: 2 other · Basis not reported
“-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”
Exact source · revision 1Source owner not reported
Evidence dependency changes (2)
- 2026-10-30T20:38:05.065Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T20:37:46.092Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalProbabilistic Energy Modeling Unifies Versatile Swarm Control
This research proposes probabilistic energy modeling as a scalable framework for decentralized swarm control, enabling zero-shot generalization across six mission classes, 10-100 agents, two hardware platforms, and environments with GPS denial. The approach uses energy terms derived from Gibbs random field minimization and structured reward maximization, allowing policies to transfer without retraining.
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