Many real-world sequential manipulation tasks involve a combination of discrete symbolic search and continuous motion planning, collectively known as combined task and motion planning (TAMP). However, prevailing methods often struggle with the computational burden and intricate combinatorial challenges, limiting their applications for online replanning in the real world. To address this, we propose Dynamic Logic-Geometric Program (D-LGP), a novel approach integrating Dynamic Tree Search and global optimization for efficient hybrid planning. Through empirical evaluation on three benchmarks, we demonstrate the efficacy of our approach, showcasing superior performance in comparison to state-of-the-art techniques. We validate our approach through simulation and demonstrate its reactive capability to cope with online uncertainty and external disturbances in the real world. Project webpage: https://sites.google.com/view/dyn-lgp.

翻译：许多现实世界中的顺序操作任务涉及离散符号搜索与连续运动规划的组合，统称为组合任务与运动规划（TAMP）。然而，现有方法常受计算负担和复杂组合挑战的制约，限制了其在现实世界在线重规划中的应用。为解决这一问题，我们提出动态逻辑几何程序（D-LGP），这是一种结合动态树搜索与全局优化的新型混合规划方法。通过在三个基准测试上的实证评估，我们展示了该方法的有效性，其在性能上优于现有最先进技术。我们通过仿真验证了该方法，并展示了其在现实世界中应对在线不确定性和外部干扰的反应能力。项目网页：https://sites.google.com/view/dyn-lgp。