In practice, the process of robot assembly in an unstructured environment faces difficulties due to the presence of unpredictable environmental errors related to vision and pose. Therefore, to minimize the uncertain environmental errors during the robotic assembly process in an unstructured environment, several studies have considered a reinforcement learning (RL)-based approach. However, if assembly parts are changed, it becomes difficult to apply RL-based methods to assemble various parts because additional learning may be required. Especially in the case of connector assembly, fine-tuning is essential because the shape changes depending on the type of connector. In this study, we propose a similar assembly state discriminator that transforms the state information (force, velocity, and RGB image) of reinforcement learning into generalized features to respond various types of connector assembly tasks. This method processes the data to include essential features for assembly regardless of connector type. By learning the RL model with the processed data using this method, the RL model trained for a specific connector can be efficiently applied to other types of connectors without fine-tuning. The assembly success rate for the 7 types of connectors (Harting, HDMI, USB, power, air jack, banana plug and PCIE) using the proposed method was demonstrated to be over 96 %.

中文翻译：

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基于强化学习的机器人连接器装配的类似装配状态鉴别器


在实践中，由于存在与视觉和姿态相关的不可预测的环境误差，非结构化环境中的机器人组装过程面临困难。因此，为了最大限度地减少非结构化环境中机器人装配过程中的不确定环境误差，一些研究考虑了基于强化学习（RL）的方法。然而，如果组装零件发生变化，则应用基于强化学习的方法来组装各种零件就会变得困难，因为可能需要额外的学习。特别是在连接器组装的情况下，微调至关重要，因为形状会根据连接器的类型而变化。在本研究中，我们提出了一种类似的装配状态鉴别器，它将强化学习的状态信息（力、速度和RGB图像）转换为通用特征，以响应各种类型的连接器装配任务。此方法处理数据以包括装配的基本特征，无论连接器类型如何。通过使用这种方法处理数据来学习 RL 模型，针对特定连接器训练的 RL 模型可以有效地应用于其他类型的连接器，而无需进行微调。使用所提出的方法，7 种类型的连接器（Harting、HDMI、USB、电源、空气插孔、香蕉插头和 PCIE）的组装成功率超过 96%。