  Forestry machines are heavy vehicles performing complex manipulation tasks in
unstructured production forest environments. Together with the complex dynamics
of the on-board hydraulically actuated cranes, the rough forest terrains have
posed a particular challenge in forestry automation. In this study, the
feasibility of applying reinforcement learning control to forestry crane
manipulators is investigated in a simulated environment. Our results show that
it is possible to learn successful actuator-space control policies for energy
efficient log grasping by invoking a simple curriculum in a deep reinforcement
learning setup. Given the pose of the selected logs, our best control policy
reaches a grasping success rate of 97%. Including an energy-optimization goal
in the reward function, the energy consumption is significantly reduced
compared to control policies learned without incentive for energy optimization,
while the increase in cycle time is marginal. The energy-optimization effects
can be observed in the overall smoother motion and acceleration profiles during
crane manipulation.
