マルチエージェント・マルチマシン保守のための物理シミュレーション
Physical Simulation for Multi-agent Multi-machine Tending
製造現場での人手不足解消に向け、簡易な卓上ロボットと強化学習を組み合わせ、シミュレーションと実機で同じ挙動が再現できるかを検証した研究。
著者: Abdalwhab Abdalwhab, Giovanni Beltrame, David St-Onge
分類: cs.RO, cs.LG
原文アブストラクト
The manufacturing sector was recently affected by workforce shortages, a problem that automation and robotics can heavily minimize. Simultaneously, reinforcement learning (RL) offers a promising solution where robots can learn through interaction with the environment. In this work, we leveraged a simplistic robotic system to work with RL with "real" data without having to deploy large expensive robots in a manufacturing setting. A real-world tabletop arena was designed with robots that mimic the agents' behavior in the simulation. Despite the difference in dynamics and machine size, the robots were able to depict the same behavior as in the simulation. In addition, those experiments provided an initial understanding of the real deployment challenges.
