Automation of hydraulic material handling machinery is currently limited to semi-static pick-and-place cycles. Dynamic throwing motions which utilize the passive joints, can greatly improve time efficiency as well as increase the dumping workspace. In this work, we use Reinforcement Learning (RL) to design dynamic controllers for material handlers with underactuated arms as commonly used in logistics. The controllers are tested both in simulation and in real-world experiments on a 12-ton test platform. The method is able to exploit the passive joints of the gripper to perform dynamic throwing motions. With the proposed controllers, the machine is able to throw individual objects to targets outside the static reachability zone with good accuracy for its practical applications. The work demonstrates the possibility of using RL to perform highly dynamic tasks with heavy machinery, suggesting a potential for improving the efficiency and precision of autonomous material handling tasks.

翻译：目前，液压物料搬运机械的自动化仅限于半静态的拾取-放置循环。利用被动关节的动态抛掷运动，可以显著提高时间效率并扩大倾倒工作空间。在本研究中，我们采用强化学习（RL）为物流领域中常用的欠驱动臂物料搬运机设计动态控制器。控制器在仿真和12吨测试平台上的真实世界实验中均进行了验证。该方法能够利用抓取器的被动关节执行动态抛掷运动。通过所提出的控制器，该机械能够将单个物体以良好的精度抛掷至静态可达区域之外的目标位置，满足实际应用需求。这项工作证明了使用强化学习执行重型机械高动态任务的可能性，表明其在提升自主物料搬运任务效率与精度方面具有潜力。