# D-LGP: Dynamic Logic-Geometric Program for Reactive Task and Motion Planning

arXiv: https://arxiv.org/abs/2312.02731 · HTML: https://mlsanity.com/paper/2312.02731/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments](https://mlsanity.com/paper/2404.16138/) (2024)
- [Bidirectional Task-Motion Planning Based on Hierarchical Reinforcement Learning for Strategic Confrontation](https://mlsanity.com/paper/2504.15876/) (2025)

## Abstract (arXiv)

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.