  Close and precise placement of irregularly shaped objects requires a skilled
robotic system. The manipulation of objects that have sensitive top surfaces
and a fixed set of neighbors is particularly challenging. To avoid damaging the
surface, the robot has to grasp them from the side, and during placement, it
has to maintain the spatial relations with adjacent objects, while considering
the physical gripper extent. In this work, we propose a framework to learn an
agent based on reinforcement learning that generates end-effector motions to
place objects as close as possible next to each other. During the placement,
our agent considers the spatial constraints with neighbors defined in a given
layout of the objects while avoiding collisions. Our approach learns to place
compact object assemblies without the need for predefined spacing between
objects as required by traditional methods. We thoroughly evaluated our
approach using a two-finger gripper mounted on a robotic arm with six degrees
of freedom. The results show that our agent significantly outperforms two
baseline approaches in object assembly compactness, thereby reducing the space
required to place the objects according to the specified spatial constraints
with the neighboring placed objects.
