Toward Optimal Tabletop Rearrangement with Multiple Manipulation Primitives

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 P M MR producing human-like, high-quality solutions with a nearly 100% success rate. Source code and supplementary materials will be available at https://github.com/arc-l/remp.