Task and Motion Planning (TAMP) algorithms can generate plans that combine logic and motion aspects for robots. However, these plans are sensitive to interference and control errors. To make TAMP algorithms more applicable and robust in the real world, we propose the m odular m ulti-level r eplanning TAMP f ramework(MMRF), expanded existing TAMP algorithms by combining real-time replanning components. MMRF generates an nominal plan from the initial state and then reconstructs this nominal plan in real-time to reorder manipulations. Following the logic-level adjustment, MMRF attempts to replan a new motion path, ensuring that the updated plan is feasible at the motion level. Finally, we conducted several real-world experiments. The result demonstrated MMRF swiftly completing tasks in scenarios with moveing obstacles and varying degrees of interference.

中文翻译：

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动态环境的模块化多级重新规划TAMP框架

任务和运动规划 (TAMP) 算法可以生成结合机器人逻辑和运动方面的计划。然而，这些计划对干扰和控制错误很敏感。为了使 TAMP 算法在现实世界中更加适用和鲁棒，我们提出模块化的多层次重新规划 TAMP框架（MMRF），通过结合实时重新规划组件扩展了现有的 TAMP 算法。 MMRF 从初始状态生成一个名义计划，然后实时重建该名义计划以重新排序操作。在逻辑级调整之后，MMRF尝试重新规划新的运动路径，确保更新的计划在运动级是可行的。最后，我们进行了几次现实世界的实验。结果表明，MMRF在有移动障碍物和不同程度干扰的场景下都能快速完成任务。