Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning

TL;DR

This paper introduces a multi-agent reinforcement learning framework for cooperative indoor human activity monitoring, directly optimizing observation accuracy with decentralized control.

Contribution

It presents a novel MARL-based approach that handles variable human counts and temporal dependencies, improving monitoring accuracy over classical methods.

Findings

Outperforms classical coverage and persistent monitoring baselines.

Robust to changes in the number of humans observed.

Effective across diverse indoor environments and tasks.

Abstract

Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization analysis. While mobile robot teams offer the potential to actively improve observation quality, existing multi-robot monitoring and active perception approaches typically rely on coverage or visitation based objectives that are weakly aligned with the accuracy requirements of human-centric monitoring tasks. In this work, we formulate cooperative active observation as a decentralized control problem in which multiple robots adjust their motion to directly optimize monitoring accuracy under partial observability. We propose a learning-based framework for cooperative policies from decentralized observations using multi-agent reinforcement learning (MARL), supported by an architecture that handles variable numbers of humans and temporal dependencies. Simulation results across diverse indoor environments and monitoring tasks show that the proposed approach consistently outperforms classical coverage, persistent monitoring, and learning-free multi-robot baselines, while remaining robust to changes in the number of observed humans.