Using digital twin to enhance Sim2real transfer for reinforcement learning in 3C assembly

Weiwen Mu, Wenbai Chen, Huaidong Zhou, Naijun Liu, Haobin Shi, Jingchen Li
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Abstract

Purpose

This paper aim to solve the problem of low assembly success rate for 3c assembly lines designed based on classical control algorithms due to inevitable random disturbances and other factors,by incorporating intelligent algorithms into the assembly line, the assembly process can be extended to uncertain assembly scenarios.

Design/methodology/approach

This work proposes a reinforcement learning framework based on digital twins. First, the authors used Unity3D to build a simulation environment that matches the real scene and achieved data synchronization between the real environment and the simulation environment through the robot operating system. Then, the authors trained the reinforcement learning model in the simulation environment. Finally, by creating a digital twin environment, the authors transferred the skill learned from the simulation to the real environment and achieved stable algorithm deployment in real-world scenarios.

Findings

In this work, the authors have completed the transfer of skill-learning algorithms from virtual to real environments by establishing a digital twin environment. On the one hand, the experiment proves the progressiveness of the algorithm and the feasibility of the application of digital twins in reinforcement learning transfer. On the other hand, the experimental results also provide reference for the application of digital twins in 3C assembly scenarios.

Originality/value

In this work, the authors designed a new encoder structure in the simulation environment to encode image information, which improved the model’s perception of the environment. At the same time, the authors used the fixed strategy combined with the reinforcement learning strategy to learn skills, which improved the rate of convergence and stability of skills learning. Finally, the authors transferred the learned skills to the physical platform through digital twin technology and realized the safe operation of the flexible printed circuit assembly task.

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利用数字孪生增强Sim2real迁移的3C装配强化学习
本文旨在解决基于经典控制算法设计的3c装配线由于不可避免的随机干扰等因素导致装配成功率低的问题,通过在装配线中引入智能算法,将装配过程扩展到不确定的装配场景。设计/方法/方法本工作提出了一种基于数字孪生的强化学习框架。首先,利用Unity3D搭建与真实场景相匹配的仿真环境,通过机器人操作系统实现真实环境与仿真环境的数据同步。然后,在仿真环境下对强化学习模型进行训练。最后,通过创建数字孪生环境,作者将从模拟中学习到的技能转移到真实环境中,并在现实场景中实现了稳定的算法部署。在这项工作中,作者通过建立一个数字孪生环境,完成了技能学习算法从虚拟环境到现实环境的转移。实验一方面证明了算法的先进性和数字孪生在强化学习迁移中应用的可行性。另一方面,实验结果也为数字孪生在3C装配场景中的应用提供了参考。在这项工作中,作者在仿真环境中设计了一种新的编码器结构来编码图像信息,提高了模型对环境的感知能力。同时,采用固定策略结合强化学习策略进行技能学习,提高了技能学习的收敛速度和稳定性。最后,通过数字孪生技术将所学技能转移到物理平台上,实现柔性印制电路组装任务的安全运行。
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