# Using Local Experiences for Global Motion Planning

arXiv: https://arxiv.org/abs/1903.08693 · HTML: https://mlsanity.com/paper/1903.08693/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [Self-Imitation Learning by Planning](https://mlsanity.com/paper/2103.13834/) (2021)
- [PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms](https://mlsanity.com/paper/2105.01777/) (2021)
- [A Survey on the Integration of Machine Learning with Sampling-based Motion Planning](https://mlsanity.com/paper/2211.08368/) (2022)
- [Waypoint Planning Networks](https://mlsanity.com/paper/2105.00312/) (2021)
- [Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning](https://mlsanity.com/paper/2306.06754/) (2023)
- [Leveraging Experience in Lifelong Multi-Agent Pathfinding](https://mlsanity.com/paper/2202.04382/) (2022)
- [CoverLib: Classifiers-equipped Experience Library by Iterative Problem Distribution Coverage Maximization for Domain-tuned Motion Planning](https://mlsanity.com/paper/2405.02968/) (2024)
- [Systematic Comparison of Path Planning Algorithms using PathBench](https://mlsanity.com/paper/2203.03092/) (2022)

## Abstract (arXiv)

Sampling-based planners are effective in many real-world applications such as robotics manipulation, navigation, and even protein modeling. However, it is often challenging to generate a collision-free path in environments where key areas are hard to sample. In the absence of any prior information, sampling-based planners are forced to explore uniformly or heuristically, which can lead to degraded performance. One way to improve performance is to use prior knowledge of environments to adapt the sampling strategy to the problem at hand. In this work, we decompose the workspace into local primitives, memorizing local experiences by these primitives in the form of local samplers, and store them in a database. We synthesize an efficient global sampler by retrieving local experiences relevant to the given situation. Our method transfers knowledge effectively between diverse environments that share local primitives and speeds up the performance dramatically. Our results show, in terms of solution time, an improvement of multiple orders of magnitude in two traditionally challenging high-dimensional problems compared to state-of-the-art approaches.