  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.
