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This paper presents a novel algorithm for robot task and motion planning(TAMP) problems by utilizing a reachability tree. While tree-based algorithmsare known for their speed and simplicity in motion planning (MP), they are notwell-suited for TAMP problems that involve both abstracted and geometricalstate variables. To address this challenge, we propose a hierarchical samplingstrategy, which first generates an abstracted task plan using Monte Carlo treesearch (MCTS) and then fills in the details with a geometrically feasiblemotion trajectory. Moreover, we show that the performance of the proposedmethod can be significantly enhanced by selecting an appropriate reward forMCTS and by using a pre-generated goal state that is guaranteed to begeometrically feasible. A comparative study using TAMP benchmark problemsdemonstrates the effectiveness of the proposed approach.

A Reachability Tree-Based Algorithm for Robot Task and Motion Planning