  This paper presents a novel algorithm for robot task and motion planning
(TAMP) problems by utilizing a reachability tree. While tree-based algorithms
are known for their speed and simplicity in motion planning (MP), they are not
well-suited for TAMP problems that involve both abstracted and geometrical
state variables. To address this challenge, we propose a hierarchical sampling
strategy, which first generates an abstracted task plan using Monte Carlo tree
search (MCTS) and then fills in the details with a geometrically feasible
motion trajectory. Moreover, we show that the performance of the proposed
method can be significantly enhanced by selecting an appropriate reward for
MCTS and by using a pre-generated goal state that is guaranteed to be
geometrically feasible. A comparative study using TAMP benchmark problems
demonstrates the effectiveness of the proposed approach.
