Teleoperation is vital in the construction industry, allowing safe machine manipulation from a distance. However, controlling machines at a joint level requires extensive training due to their complex degrees of freedom. Task space control offers intuitive maneuvering, but precise control often requires dynamic models, posing challenges for hydraulic machines. To address this, we use a data-driven actuator model to capture machine dynamics in real-world operations. By integrating this model into simulation and reinforcement learning, an optimal control policy for task space control is obtained. Experiments with Brokk 170 validate the framework, comparing it to a well-known Jacobian-based approach.

翻译：远程操纵在建筑行业中至关重要，可确保设备远距离安全操控。然而，由于液压工程机械具有复杂的自由度，关节级控制需要大量培训。任务空间控制提供直观的操作方式，但其精准控制往往依赖动力学模型，这对液压机械提出了挑战。为此，我们采用数据驱动执行器模型来捕捉实际作业中的机械动态特性。通过将该模型集成到仿真与强化学习中，获得了用于任务空间控制的最优控制策略。利用Brokk 170进行的实验验证了该框架的性能，并与著名的基于雅可比矩阵的方法进行了对比。