動的屋内環境監視のためのマルチエージェント強化学習による協調的情報収集
Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning
複数ロボットが屋内の人間活動を監視する際、監視精度を直接最適化する協調制御をマルチエージェント強化学習で学習する手法を提案した。
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著者: Kanghoon Lee, Matthew M. Sato, Jinnyeong Yang, Seungro Lee, Sujin Lee, Jiachen Li, Kuk-Jin Yoon, Jinkyoo Park, Kincho H. Law, Yoonjin Yoon
分類: cs.RO, cs.AI, cs.MA
原文アブストラクト
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.
