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In practice, many types of manipulation actions (e.g., pick-n-place and push)are needed to accomplish real-world manipulation tasks. Yet, limited researchexists that explores the synergistic integration of different manipulationactions for optimally solving long-horizon task-and-motion planning problems.In this study, we propose and investigate planning high-quality actionsequences for solving long-horizon tabletop rearrangement tasks in whichmultiple manipulation primitives are required. Denoting the problemrearrangement with multiple manipulation primitives (REMP), we develop twoalgorithms, hierarchical best-first search (HBFS) and parallel Monte Carlo treesearch for multi-primitive rearrangement (PMMR) toward optimally resolving thechallenge. Extensive simulation and real robot experiments demonstrate thatboth methods effectively tackle REMP, with HBFS excelling in planning speed andPMMR producing human-like, high-quality solutions with a nearly 100% successrate.

Toward Optimal Tabletop Rearrangement with Multiple Manipulation Primitives