  Extraterrestrial rovers with a general-purpose robotic arm have many
potential applications in lunar and planetary exploration. Introducing autonomy
into such systems is desirable for increasing the time that rovers can spend
gathering scientific data and collecting samples. This work investigates the
applicability of deep reinforcement learning for vision-based robotic grasping
of objects on the Moon. A novel simulation environment with
procedurally-generated datasets is created to train agents under challenging
conditions in unstructured scenes with uneven terrain and harsh illumination. A
model-free off-policy actor-critic algorithm is then employed for end-to-end
learning of a policy that directly maps compact octree observations to
continuous actions in Cartesian space. Experimental evaluation indicates that
3D data representations enable more effective learning of manipulation skills
when compared to traditionally used image-based observations. Domain
randomization improves the generalization of learned policies to novel scenes
with previously unseen objects and different illumination conditions. To this
end, we demonstrate zero-shot sim-to-real transfer by evaluating trained agents
on a real robot in a Moon-analogue facility.
