论文标题
掌握Fusion应用程序中的自主组装与学习：钉上孔研究
Mastering Autonomous Assembly in Fusion Application with Learning-by-doing: a Peg-in-hole Study
论文作者
论文摘要
机器人钉孔组装代表了机器人自动化中的关键研究领域。增强学习（RL）和深神经网络（DNN）的融合在这一领域取得了显着突破。但是，现有的基于RL的方法努力在融合应用程序的独特环境和任务限制下提供最佳性能。结果，我们提出了一种创新设计的基于RL的方法。与替代方法相反，我们的重点是增强DNN体系结构而不是RL模型。我们的策略从RGB摄像机和力/扭矩（F/T）传感器中接收并集成了数据，训练代理以类似于人的手眼协调的方式执行钉孔装配任务。所有训练和实验都在现实的环境中展开，经验结果表明，这种多传感器融合方法在刚性钉孔的组装任务中出色，超过了使用的机器人臂的可重复准确性-0.1 mm-在不确定和不稳定的条件下。
Robotic peg-in-hole assembly represents a critical area of investigation in robotic automation. The fusion of reinforcement learning (RL) and deep neural networks (DNNs) has yielded remarkable breakthroughs in this field. However, existing RL-based methods grapple with delivering optimal performance under the unique environmental and mission constraints of fusion applications. As a result, we propose an inventively designed RL-based approach. In contrast to alternative methods, our focus centers on enhancing the DNN architecture rather than the RL model. Our strategy receives and integrates data from the RGB camera and force/torque (F/T) sensor, training the agent to execute the peg-in-hole assembly task in a manner akin to human hand-eye coordination. All training and experimentation unfold within a realistic environment, and empirical outcomes demonstrate that this multi-sensor fusion approach excels in rigid peg-in-hole assembly tasks, surpassing the repeatable accuracy of the robotic arm utilized--0.1 mm--in uncertain and unstable conditions.
