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.

翻译：机器人轴孔装配是机器人自动化领域的关键研究方向。强化学习与深度神经网络的融合已在该领域取得显著突破。然而，现有基于强化学习的方法在聚变应用的特定环境与任务约束下难以实现最优性能。为此，我们提出了一种创新设计的基于强化学习的方法。与其它方法不同，我们重点优化深度神经网络架构而非强化学习模型本身。该策略接收并融合RGB相机与力/扭矩传感器的多模态数据，以类似人类手眼协调的方式训练智能体执行轴孔装配任务。所有训练与实验均在实际环境中进行，实证结果表明，这种多传感器融合方法在刚性轴孔装配任务中表现优异，能在不确定和不稳定条件下超越所用机械臂的重复定位精度（0.1毫米）。