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 more applicable in real-world, we propose the modular multi-level replanning TAMP framework(MMRF), blending the probabilistic completeness of sampling-based TAMP algorithm with the robustness of reactive replanning. MMRF generates an nominal plan from the initial state, then dynamically reconstructs this nominal plan in real-time, reorders robot manipulations. Following the logic-level adjustment, GMRF will try to replan a new motion path to ensure the updated plan is feasible at the motion level. Finally, we conducted real-world experiments involving stack and rearrange task domains. The result demonstrate MMRF's ability to swiftly complete tasks in scenarios with varying degrees of interference.

翻译：任务与运动规划算法能够为机器人生成融合逻辑与运动层面的规划方案。然而，这些规划方案易受干扰与控制误差影响。为提升TAMP在现实环境中的适用性，我们提出模块化多层次重规划TAMP框架，该框架融合了基于采样的TAMP算法的概率完备性与反应式重规划的鲁棒性。MMRF首先从初始状态生成名义规划，随后实时动态重构该名义规划并重新编排机器人操作序列。在逻辑层级调整完成后，MMRF将尝试重规划新的运动路径，以确保更新后的规划在运动层级具有可行性。最终，我们通过在堆叠与重排两类任务域中的真实环境实验，验证了MMRF在多种干扰强度场景下快速完成任务的能力。