SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control

arXiv:2608.06587 · cs.RO, cs.AI · Submitted 2026-08-06 · Read on arXiv

Varun Raveendra, Connor Mattson, Daniel S. Brown

cs.RO, cs.AI

Submitted: 2026-08-06

Updated: 2026-08-10

Comments: 8 pages, 10 figures, IROS 2026

License: http://creativecommons.org/licenses/by/4.0/

The gist: Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control.

Terminology

Boolean Gossip — 10× in this paper
Accuracy — 6× in this paper · explained in 1 episode
Event-Triggered Communication — 4× in this paper
Calibration — 3× in this paper · explained in 9 episodes
Communication Overhead — 3× in this paper · explained in 2 episodes
Integrated Belief Synchronizer — 3× in this paper
Multi-Agent System — 2× in this paper · explained in 7 episodes
Robustness — 2× in this paper · explained in 6 episodes
Swarm Behavior Classifier — 2× in this paper
Closed-Loop Control — 1× in this paper · explained in 3 episodes
Communication Efficiency — 1× in this paper · explained in 2 episodes
Conformal Prediction — 1× in this paper · explained in 11 episodes
Consistency — 1× in this paper · explained in 3 episodes
Cooperation — 1× in this paper · explained in 2 episodes
F1 Score — 1× in this paper · explained in 4 episodes
Representation Learning — 1× in this paper · explained in 3 episodes
Temporal Convolutional Network — 1× in this paper
Time Series Forecasting — 1× in this paper · explained in 4 episodes
Transformer — 1× in this paper · explained in 2 episodes
Transformer Model — 1× in this paper · explained in 2 episodes
Transformer Models — 1× in this paper · explained in 3 episodes

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.

SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control

Terminology

Abstract

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