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：机器人插销入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在该领域取得了卓越突破。然而，现有基于强化学习的方法在聚变应用的独特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。与其他方法不同，我们专注于改进深度神经网络架构而非强化学习模型本身。我们的策略接收并融合来自RGB相机和力/扭矩传感器的数据，训练智能体以类似人类手眼协调的方式执行插销入孔装配任务。所有训练与实验均在逼真环境中进行，实证结果表明，这种多传感器融合方法在刚性插销入孔装配任务中表现优异，在不确定性和不稳定性条件下超越了所用机械臂的可重复精度（0.1毫米）。