  In the spatial action representation, the action space spans the space of
target poses for robot motion commands, i.e. SE(2) or SE(3). This approach has
been used to solve challenging robotic manipulation problems and shows promise.
However, the method is often limited to a three dimensional action space and
short horizon tasks. This paper proposes ASRSE3, a new method for handling
higher dimensional spatial action spaces that transforms an original MDP with
high dimensional action space into a new MDP with reduced action space and
augmented state space. We also propose SDQfD, a variation of DQfD designed for
large action spaces. ASRSE3 and SDQfD are evaluated in the context of a set of
challenging block construction tasks. We show that both methods outperform
standard baselines and can be used in practice on real robotics systems.
