  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.
