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结合，统称为组合任务与运动规划（TAMP）。然而，现有方法常受困于计算负担和复杂的组合挑战，限制了其在现实环境中进行在线重规划的应用。为此，我们提出动态逻辑几何程序（D-LGP），一种融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，我们验证了该方法的有效性，展示了其相较于现有最先进技术的优越性能。我们通过仿真验证了该方法，并证明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。