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

2024-01-10

arXiv.org

AI Summary

To address the challenge of collaborative coverage monitoring by decentralized robot swarms in unknown environments under communication and sensing constraints, this paper proposes an end-to-end trainable perception–action–communication closed-loop architecture. The method innovatively integrates a CNN for local observation processing, a GNN for dynamic topology modeling and adaptive communication-content selection and fusion strategy learning, and a shallow MLP for generating distributed control actions—enabling their joint optimization for the first time. Trained via imitation learning, the framework significantly outperforms classical centralized and distributed baselines in coverage efficiency. It exhibits strong generalization (to unseen environments), scalability (seamless deployment on larger swarms), and robustness (stable performance under positional estimation noise).

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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 convolution 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.

Problem

• Coverage control for robot swarms monitoring unknown environments
• Decentralized navigation with limited communication and sensing capabilities
• Learning collaborative perception-action-communication loops for swarm coordination

Innovation

• CNN processes localized perception for robot navigation
• GNN facilitates robot communications for swarm collaboration
• MLP computes robot actions in decentralized coverage control