# Learn and Link: Learning Critical Regions for Efficient Planning

arXiv: https://arxiv.org/abs/1903.03258 · HTML: https://mlsanity.com/paper/1903.03258/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

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

- [Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks](https://mlsanity.com/paper/2301.04330/) (2023)
- [Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning](https://mlsanity.com/paper/2202.00907/) (2022)
- [Direct Robot Configuration Space Construction using Convolutional Encoder-Decoders](https://mlsanity.com/paper/2303.05653/) (2023)
- [Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network](https://mlsanity.com/paper/2502.15662/) (2025)
- [Learning Continuous Cost-to-Go Functions for Non-holonomic Systems](https://mlsanity.com/paper/2103.11168/) (2021)
- [Multi-Task Option Learning and Discovery for Stochastic Path Planning](https://mlsanity.com/paper/2210.00068/) (2022)
- [From domain-landmark graph learning to problem-landmark graph generation](https://mlsanity.com/paper/2509.17062/) (2025)

## Abstract (arXiv)

This paper presents a new approach to learning for motion planning (MP) where critical regions of an environment are learned from a given set of motion plans and used to improve performance on new environments and problem instances. We introduce a new suite of sampling-based motion planners, Learn and Link. Our planners leverage critical regions to overcome the limitations of uniform sampling, while still maintaining guarantees of correctness inherent to sampling-based algorithms. We also show that convolutional neural networks (CNNs) can be used to identify critical regions for motion planning problems. We evaluate Learn and Link against planners from the Open Motion Planning Library (OMPL) using an extensive suite of experiments on challenging motion planning problems. We show that our approach requires far less planning time than existing sampling-based planners.