用仿真训练多机器人协作，真实桌面系统验证效果。
Physical Simulation for Multi-agent Multi-machine Tending
- 在仿真中用强化学习训练多机器人协同作业。
- 真实桌面平台复现仿真行为，验证迁移可行性。
- 揭示了真实部署中的动态差异与挑战。
制造行业近期受劳动力短缺影响，自动化与机器人技术可显著缓解此问题。同时，强化学习（RL）提供了一种解决方案，使机器人通过与环境交互自主学习。本文采用简化机器人系统，在无需部署昂贵大型机器人的情况下，利用真实数据进行强化学习训练。设计了一个真实的桌面实验场，其中机器人模拟仿真中的代理行为。尽管动力学特性与机器尺寸存在差异，机器人仍能实现与仿真中一致的行为表现。此外，这些实验为真实部署中的挑战提供了初步理解。
原文摘要 · Abstract (English)
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.
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