The precise and safe control of heavy material handling machines presents numerous challenges due to the hard-to-model hydraulically actuated joints and the need for collision-free trajectory planning with a free-swinging end-effector tool. In this work, we propose an RL-based controller that commands the cabin joint and the arm simultaneously. It is trained in a simulation combining data-driven modeling techniques with first-principles modeling. On the one hand, we employ a neural network model to capture the highly nonlinear dynamics of the upper carriage turn hydraulic motor, incorporating explicit pressure prediction to handle delays better. On the other hand, we model the arm as velocity-controllable and the free-swinging end-effector tool as a damped pendulum using first principles. This combined model enhances our simulation environment, enabling the training of RL controllers that can be directly transferred to the real machine. Designed to reach steady-state Cartesian targets, the RL controller learns to leverage the hydraulic dynamics to improve accuracy, maintain high speeds, and minimize end-effector tool oscillations. Our controller, tested on a mid-size prototype material handler, is more accurate than an inexperienced operator and causes fewer tool oscillations. It demonstrates competitive performance even compared to an experienced professional driver.

翻译：重型物料搬运机械的精确安全控制面临诸多挑战，原因在于其液压驱动关节难以建模，且需为自由摆动的末端执行器工具规划无碰撞轨迹。本研究提出一种基于强化学习（RL）的控制器，可同时指令驾驶室关节与机械臂动作。该控制器在融合数据驱动建模技术与第一性原理建模的仿真环境中训练完成。一方面，我们采用神经网络模型捕捉上部回转液压马达的高度非线性动力学特性，通过引入显式压力预测以更好地处理延迟效应；另一方面，我们基于第一性原理将机械臂建模为速度可控系统，并将自由摆动的末端执行器工具建模为阻尼摆。这种组合模型增强了仿真环境的真实性，使得训练出的RL控制器能够直接迁移至真实机械。该RL控制器以实现稳态笛卡尔空间目标为设计导向，通过学习利用液压动力学特性来提高定位精度、保持高速运行并最小化末端执行器工具的摆动幅度。在中等尺寸原型物料搬运机上进行的测试表明，我们的控制器比缺乏经验的操作员具有更高精度，且引发的工具摆动更少。即使与经验丰富的专业驾驶员相比，其性能也展现出竞争力。