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 stemming from the multitude of action skeletons. 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 capability for online replanning under uncertainty and external disturbances in the real world.

翻译：许多真实世界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，这一领域统称为任务与运动联合规划（TAMP）。然而，现有方法常因大量动作骨架带来的计算负担与复杂组合挑战而效果有限。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，我们证明了该方法相对于最先进技术的优越性能。此外，我们通过仿真验证了该方法在不确定性与外部扰动环境中的实时在线重规划能力。