  Effectively rearranging heterogeneous objects constitutes a high-utility
skill that an intelligent robot should master. Whereas significant work has
been devoted to the grasp synthesis of heterogeneous objects, little attention
has been given to the planning for sequentially manipulating such objects. In
this work, we examine the long-horizon sequential rearrangement of
heterogeneous objects in a tabletop setting, addressing not just generating
feasible plans but near-optimal ones. Toward that end, and building on previous
methods, including combinatorial algorithms and Monte Carlo tree search-based
solutions, we develop state-of-the-art solvers for optimizing two practical
objective functions considering key object properties such as size and weight.
Thorough simulation studies show that our methods provide significant
advantages in handling challenging heterogeneous object rearrangement problems,
especially in cluttered settings. Real robot experiments further demonstrate
and confirm these advantages.
  Source code and evaluation data associated with this research will be
available at https://github.com/arc-l/TRLB upon the publication of this
manuscript.
