  In practice, many types of manipulation actions (e.g., pick-n-place and push)
are needed to accomplish real-world manipulation tasks. Yet, limited research
exists that explores the synergistic integration of different manipulation
actions for optimally solving long-horizon task-and-motion planning problems.
In this study, we propose and investigate planning high-quality action
sequences for solving long-horizon tabletop rearrangement tasks in which
multiple manipulation primitives are required. Denoting the problem
rearrangement with multiple manipulation primitives (REMP), we develop two
algorithms, hierarchical best-first search (HBFS) and parallel Monte Carlo tree
search for multi-primitive rearrangement (PMMR) toward optimally resolving the
challenge. Extensive simulation and real robot experiments demonstrate that
both methods effectively tackle REMP, with HBFS excelling in planning speed and
PMMR producing human-like, high-quality solutions with a nearly 100% success
rate.
