  Excavation of irregular rigid objects in clutter, such as fragmented rocks
and wood blocks, is very challenging due to their complex interaction dynamics
and highly variable geometries. In this paper, we adopt reinforcement learning
(RL) to tackle this challenge and learn policies to plan for a sequence of
excavation trajectories for irregular rigid objects, given point clouds of
excavation scenes. Moreover, we separately learn a compact representation of
the point cloud on geometric tasks that do not require human labeling. We show
that using the representation reduces training time for RL, while achieving
similar asymptotic performance compare to an end-to-end RL algorithm. When
using a policy trained in simulation directly on a real scene, we show that the
policy trained with the representation outperforms end-to-end RL. To our best
knowledge, this paper presents the first application of RL to plan a sequence
of excavation trajectories of irregular rigid objects in clutter.
