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 generalized multi-level replanning TAMP framework(GMRF), blending the probabilistic completeness of sampling-based TAMP algorithm with the robustness of reactive replanning. GMRF 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 GMRF's ability to swiftly complete tasks in scenarios with varying degrees of interference.

翻译：任务与运动规划（Task and Motion Planning，TAMP）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，这些方案易受干扰与控制误差的影响。为使TAMP更适用于实际场景，我们提出广义多层重规划TAMP框架（Generalized Multi-Level Replanning TAMP Framework，GMRF），将基于采样的TAMP算法的概率完备性与反应式重规划的鲁棒性相结合。GMRF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排序机器人操作动作。在逻辑层面调整后，GMRF将尝试重新规划新的运动路径，以确保更新后的规划方案在运动层面具有可行性。最后，我们开展了涉及堆叠与重排列任务领域的实际实验。结果表明，GMRF能够在不同干扰程度的场景中快速完成任务。