# MPTP: Motion-Planning-aware Task Planning for Navigation in Belief Space

arXiv: https://arxiv.org/abs/2104.04696 · HTML: https://mlsanity.com/paper/2104.04696/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 61 references that are in the map.
- First posted on arXiv 10 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Online Replanning in Belief Space for Partially Observable Task and Motion Problems](https://mlsanity.com/paper/1911.04577/) (2019)
- [Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systems](https://mlsanity.com/paper/1804.08229/) (2018)
- [FFRob: Leveraging Symbolic Planning for Efficient Task and Motion Planning](https://mlsanity.com/paper/1608.01335/) (2016)
- [Towards Multi-Robot Task-Motion Planning for Navigation in Belief Space](https://mlsanity.com/paper/2010.00780/) (2020)
- [Towards Combining HTN Planning and Geometric Task Planning](https://mlsanity.com/paper/1307.1482/) (2013)

## Built on by (most cited first, 7 of 7)

- [Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation](https://mlsanity.com/paper/2302.07114/) (2023)
- [Minimum Displacement Motion Planning for Movable Obstacles](https://mlsanity.com/paper/2204.12740/) (2022)
- [A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks](https://mlsanity.com/paper/2503.07700/) (2025)
- [Locally Optimal Solutions to Constraint Displacement Problems via Path-Obstacle Overlaps](https://mlsanity.com/paper/2511.12203/) (2025)
- [A Framework for Task and Motion Planning based on Expanding AND/OR Graphs](https://mlsanity.com/paper/2509.00317/) (2025)
- [Learning Motion Feasibility from Point Clouds in Cluttered Environments](https://mlsanity.com/paper/2606.26700/) (2026)
- [A Horizon-slicing Approach to Minimum Obstacle Displacement Planning for Robot Navigation](https://mlsanity.com/paper/2609.22974/) (2026)

## Abstract (arXiv)

We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environments. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In this paper, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our approach by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work.