# LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control

arXiv: https://arxiv.org/abs/2401.04855 · HTML: https://mlsanity.com/paper/2401.04855/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 61 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://mlsanity.com/paper/2003.01060/) (2020)
- [CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction](https://mlsanity.com/paper/1704.03489/) (2017)
- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [Graph Neural Networks: Architectures, Stability and Transferability](https://mlsanity.com/paper/2008.01767/) (2020)
- [Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks](https://mlsanity.com/paper/1903.10527/) (2019)
- [Augmenting Visual Place Recognition with Structural Cues](https://mlsanity.com/paper/2003.00278/) (2020)
- [Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks](https://mlsanity.com/paper/2011.01119/) (2020)
- [Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection](https://mlsanity.com/paper/2105.08601/) (2021)
- [Coverage Control in Multi-Robot Systems via Graph Neural Networks](https://mlsanity.com/paper/2109.15278/) (2021)
- [Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning](https://mlsanity.com/paper/2012.14906/) (2020)
- [Graph Neural Networks for Multi-Robot Active Information Acquisition](https://mlsanity.com/paper/2209.12091/) (2022)
- [A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies](https://mlsanity.com/paper/2111.01777/) (2021)
- [ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture](https://mlsanity.com/paper/2103.13446/) (2021)
- [Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks](https://mlsanity.com/paper/2106.13358/) (2021)
- [With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance](https://mlsanity.com/paper/2009.12106/) (2020)
- [Multi-Robot Gaussian Process Estimation and Coverage: A Deterministic Sequencing Algorithm and Regret Analysis](https://mlsanity.com/paper/2101.04306/) (2021)
- [Multi-Robot Control Using Time-Varying Density Functions](https://mlsanity.com/paper/1404.0338/) (2014)
- [Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense](https://mlsanity.com/paper/2301.09689/) (2023)
- [A Scalable Multi-Robot Framework for Decentralized and Asynchronous Perception-Action-Communication Loops](https://mlsanity.com/paper/2309.10164/) (2023)

## Built on by (most cited first, 3 of 3)

- [Scalable Multi Agent Diffusion Policies for Coverage Control](https://mlsanity.com/paper/2509.17244/) (2025)
- [Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning](https://mlsanity.com/paper/2409.19829/) (2024)
- [Controlling Collectives of AI Agents in Reasoning Space with Spatial Transformers](https://mlsanity.com/paper/2609.28247/) (2026)

## Abstract (arXiv)

Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known a priori. The problem is challenging in decentralized settings with robots that have limited communication and sensing capabilities. We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem, wherein a convolutional neural network (CNN) processes localized perception; a graph neural network (GNN) facilitates robot communications; finally, a shallow multi-layer perceptron (MLP) computes robot actions. The GNN enables collaboration in the robot swarm by computing what information to communicate with nearby robots and how to incorporate received information. Evaluations show that the LPAC models -- trained using imitation learning -- outperform standard decentralized and centralized coverage control algorithms. The learned policy generalizes to environments different from the training dataset, transfers to larger environments with more robots, and is robust to noisy position estimates. The results indicate the suitability of LPAC architectures for decentralized navigation in robot swarms to achieve collaborative behavior.