# RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning

arXiv: https://arxiv.org/abs/2110.03420 · HTML: https://mlsanity.com/paper/2110.03420/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2021.

## Built on by (most cited first, 6 of 6)

- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search](https://mlsanity.com/paper/2506.07062/) (2025)
- [Efficient Learning of High Level Plans from Play](https://mlsanity.com/paper/2303.09628/) (2023)
- [Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments](https://mlsanity.com/paper/2404.16138/) (2024)
- [Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planning](https://mlsanity.com/paper/2409.05586/) (2024)
- [Bidirectional Task-Motion Planning Based on Hierarchical Reinforcement Learning for Strategic Confrontation](https://mlsanity.com/paper/2504.15876/) (2025)

## Abstract (arXiv)

Sequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many objects that can be interacted with, planning efficiency becomes even more important. To plan such long-horizon tasks, we present the RHH-LGP algorithm for combined task and motion planning (TAMP). First, we propose a TAMP approach (based on Logic-Geometric Programming) that effectively uses geometry-based heuristics for solving long-horizon manipulation tasks. The efficiency of this planner is then further improved by a receding horizon formulation, resulting in RHH-LGP. We demonstrate the robustness and effectiveness of our approach on a diverse range of long-horizon tasks that require reasoning about interactions with a large number of objects. Using our framework, we can solve tasks that require multiple robots, including a mobile robot and snake-like walking robots, to form novel heterogeneous kinematic structures autonomously. By combining geometry-based heuristics with iterative planning, our approach brings an order-of-magnitude reduction of planning time in all investigated problems.