Probabilistic energy modeling enables flexible control of robotic swarms
Probabilistic Energy Modeling Unifies Versatile Swarm Control
RoboticsMultiagent Systems
Summary
Controlling groups of robots that work together can be tricky because changing their tasks often means redesigning their control systems. The authors show a way to represent robot swarm behaviors with a shared energy-based model, letting many missions happen without redesign or retraining. They train simple control policies for basic behaviors and then combine them to handle new tasks instantly, even on different numbers of robots and in challenging environments. Tests included flying drone swarms indoors and outdoors, with strong performance both in simulations and real-world setups.
What this means in practice
- •For drone fleet operators: Deploy aerial drone swarms on multiple missions without needing to redesign or retrain control policies for each new task or environment.$Commercial implications: Enables flexible drone mission software that works across different swarm sizes and hardware, appealing to commercial fleet service providers.
- •For robotics software developers: Build reusable, modular swarm control policies that combine basic behaviors into complex missions, reducing development time and effort.
Authors
Xiang Zhou, Xiangyu Zhu, Xintong Wang, Qingyuan Yang, Rui Huang, Feng Xue, Lin Li, Qingrui Zhang
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.