SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control
arXiv:2608.06587v1 Announce Type: new Abstract: Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control. However, little research explores how agents can infer swarm-level behavior from purely local perception, a capability critical for detecting faults and behavior changes. In this paper, we introduce Synchronized Swarm Behavior Classification (SyncSBC), which combines improvements in machine learning and distributed consensus to classify collective swarm behavior and synchronize swarm decision-making in an entirely decentralized manner. We show that SyncSBC achieves high classification accuracy and low synchronization delay, making it suitable for real-world deployment. Finally, we use SyncSBC to demonstrate two promising swarm applications on real robots where we show that swarms utilizing SyncSBC can accurately identify anomalies in robot behavior and autonomously coordinate collective changes in swarm behavior. Videos, code and supplemental experiments are available at https://sites.google.com/view/sync-sbc/home.
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[Submitted on 6 Aug 2026]
Title:SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control
View a PDF of the paper titled SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control, by Varun Raveendra and 2 other authors
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Abstract:Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control. However, little research explores how agents can infer swarm-level behavior from purely local perception, a capability critical for detecting faults and behavior changes. In this paper, we introduce Synchronized Swarm Behavior Classification (SyncSBC), which combines improvements in machine learning and distributed consensus to classify collective swarm behavior and synchronize swarm decision-making in an entirely decentralized manner. We show that SyncSBC achieves high classification accuracy and low synchronization delay, making it suitable for real-world deployment. Finally, we use SyncSBC to demonstrate two promising swarm applications on real robots where we show that swarms utilizing SyncSBC can accurately identify anomalies in robot behavior and autonomously coordinate collective changes in swarm behavior. Videos, code and supplemental experiments are available at this https URL.
Comments: 8 pages, 10 figures, IROS 2026
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.06587 [cs.RO]
(or arXiv:2608.06587v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.06587
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Varun Raveendra [view email] [v1] Thu, 6 Aug 2026 20:59:04 UTC (4,235 KB)
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