  We explore multi-log grasping using reinforcement learning and virtual visual
servoing for automated forwarding in a simulated environment. Automation of
forest processes is a major challenge, and many techniques regarding robot
control pose different challenges due to the unstructured and harsh outdoor
environment. Grasping multiple logs involves various problems of dynamics and
path planning, where understanding the interaction between the grapple, logs,
terrain, and obstacles requires visual information. To address these
challenges, we separate image segmentation from crane control and utilise a
virtual camera to provide an image stream from reconstructed 3D data. We use
Cartesian control to simplify domain transfer to real-world applications. Since
log piles are static, visual servoing using a 3D reconstruction of the pile and
its surroundings is equivalent to using real camera data until the point of
grasping. This relaxes the limits on computational resources and time for the
challenge of image segmentation and allows for collecting data in situations
where the log piles are not occluded. The disadvantage is the lack of
information during grasping. We demonstrate that this problem is manageable and
present an agent that is 95% successful in picking one or several logs from
challenging piles of 2--5 logs.
