Symbolic task planning for robots is computationally challenging due to the combinatorial complexity of the possible action space. This fact is amplified if there are several sub-goals to be achieved due to the increased length of the action sequences. In this work, we propose a multi-goal symbolic task planner for deterministic decision processes based on Monte Carlo Tree Search. We augment the algorithm by prioritized node expansion which prioritizes nodes that already have fulfilled some sub-goals. Due to its linear complexity in the number of sub-goals, our algorithm is able to identify symbolic action sequences of 145 elements to reach the desired goal state with up to 48 sub-goals while the search tree is limited to under 6500 nodes. We use action reduction based on a kinematic reachability criterion to further ease computational complexity. We combine our algorithm with object localization and motion planning and apply it to a real-robot demonstration with two manipulators in an industrial bearing inspection setting.

翻译：机器人符号任务规划因可能的动作空间具有组合复杂性而面临巨大计算挑战。若需实现多个子目标，动作序列长度增加将进一步加剧该问题。本文提出一种基于蒙特卡洛树搜索的确定性决策过程多目标符号任务规划器。我们通过优先级节点扩展增强算法，优先扩展已达成部分子目标的节点。由于算法复杂度与子目标数量呈线性关系，在搜索树规模限制在6500个节点以内时，能够识别包含145个元素的符号动作序列，实现最多48个子目标的目标状态。我们基于运动学可达性准则进行动作约减，以进一步降低计算复杂度。将该算法与目标定位及运动规划相结合，并在工业轴承检测场景中利用双机械臂实现真实机器人演示验证。