  Robots need the capability of placing objects in arbitrary, specific poses to
rearrange the world and achieve various valuable tasks. Object reorientation
plays a crucial role in this as objects may not initially be oriented such that
the robot can grasp and then immediately place them in a specific goal pose. In
this work, we present a vision-based manipulation system, ReorientBot, which
consists of 1) visual scene understanding with pose estimation and volumetric
reconstruction using an onboard RGB-D camera; 2) learned waypoint selection for
successful and efficient motion generation for reorientation; 3) traditional
motion planning to generate a collision-free trajectory from the selected
waypoints. We evaluate our method using the YCB objects in both simulation and
the real world, achieving 93% overall success, 81% improvement in success rate,
and 22% improvement in execution time compared to a heuristic approach. We
demonstrate extended multi-object rearrangement showing the general capability
of the system.
