# Path Database Guidance for Motion Planning

arXiv: https://arxiv.org/abs/2504.05550 · HTML: https://mlsanity.com/paper/2504.05550/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [Motion Planning Networks](https://mlsanity.com/paper/1806.05767/) (2018)
- [Robot Motion Planning in Learned Latent Spaces](https://mlsanity.com/paper/1807.10366/) (2018)
- [Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions](https://mlsanity.com/paper/2010.15335/) (2020)
- [Experience-Based Planning with Sparse Roadmap Spanners](https://mlsanity.com/paper/1410.1950/) (2014)
- [Memory of Motion for Warm-starting Trajectory Optimization](https://mlsanity.com/paper/1907.01474/) (2019)
- [Learning to Retrieve Relevant Experiences for Motion Planning](https://mlsanity.com/paper/2204.08550/) (2022)
- [Learning the Problem-Optimum Map: Analysis and Application to Global Optimization in Robotics](https://mlsanity.com/paper/1605.04636/) (2016)
- [Path Planning for Manipulation using Experience-driven Random Trees](https://mlsanity.com/paper/2103.00448/) (2021)
- [A Framework for Guided Motion Planning](https://mlsanity.com/paper/2404.03133/) (2024)
- [CoverLib: Classifiers-equipped Experience Library by Iterative Problem Distribution Coverage Maximization for Domain-tuned Motion Planning](https://mlsanity.com/paper/2405.02968/) (2024)

## Abstract (arXiv)

One approach to using prior experience in robot motion planning is to store solutions to previously seen problems in a database of paths. Methods that use such databases are characterized by how they query for a path and how they use queries given a new problem. In this work we present a new method, Path Database Guidance (PDG), which innovates on existing work in two ways. First, we use the database to compute a heuristic for determining which nodes of a search tree to expand, in contrast to prior work which generally pastes the (possibly transformed) queried path or uses it to bias a sampling distribution. We demonstrate that this makes our method more easily composable with other search methods by dynamically interleaving exploration according to a baseline algorithm with exploitation of the database guidance. Second, in contrast to other methods that treat the database as a single fixed prior, our database (and thus our queried heuristic) updates as we search the implicitly defined robot configuration space. We experimentally demonstrate the effectiveness of PDG in a variety of explicitly defined environment distributions in simulation.