# A Survey on the Integration of Machine Learning with Sampling-based Motion Planning

arXiv: https://arxiv.org/abs/2211.08368 · HTML: https://mlsanity.com/paper/2211.08368/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 125 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 30 of 53)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [Learning Sampling Distributions for Robot Motion Planning](https://mlsanity.com/paper/1709.05448/) (2017)
- [Motion Planning Networks](https://mlsanity.com/paper/1806.05767/) (2018)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference](https://mlsanity.com/paper/1707.07383/) (2017)
- [Learning compositional models of robot skills for task and motion planning](https://mlsanity.com/paper/2006.06444/) (2020)
- [Robot Motion Planning in Learned Latent Spaces](https://mlsanity.com/paper/1807.10366/) (2018)
- [Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances](https://mlsanity.com/paper/2108.04145/) (2021)
- [Learning Heuristic Search via Imitation](https://mlsanity.com/paper/1707.03034/) (2017)
- [Learning to guide task and motion planning using score-space representation](https://mlsanity.com/paper/1807.09962/) (2018)
- [Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning](https://mlsanity.com/paper/2010.06491/) (2020)
- [Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions](https://mlsanity.com/paper/1306.3532/) (2013)
- [Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Search](https://mlsanity.com/paper/1707.01888/) (2017)
- [Harnessing Reinforcement Learning for Neural Motion Planning](https://mlsanity.com/paper/1906.00214/) (2019)
- [RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies](https://mlsanity.com/paper/1907.04799/) (2019)
- [LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning](https://mlsanity.com/paper/1907.09574/) (2019)
- [Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees](https://mlsanity.com/paper/1903.00070/) (2019)
- [Learned Critical Probabilistic Roadmaps for Robotic Motion Planning](https://mlsanity.com/paper/1910.03701/) (2019)
- [Asymptotically Optimal Sampling-based Kinodynamic Planning](https://mlsanity.com/paper/1407.2896/) (2014)
- [Learning-Based Proxy Collision Detection for Robot Motion Planning Applications](https://mlsanity.com/paper/1902.08164/) (2019)
- [Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks](https://mlsanity.com/paper/2210.08864/) (2022)
- [Generalized Lazy Search for Robot Motion Planning: Interleaving Search and Edge Evaluation via Event-based Toggles](https://mlsanity.com/paper/1904.02795/) (2019)
- [MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets](https://mlsanity.com/paper/2112.06402/) (2021)
- [Neural Manipulation Planning on Constraint Manifolds](https://mlsanity.com/paper/2008.03787/) (2020)
- [Learning Obstacle Representations for Neural Motion Planning](https://mlsanity.com/paper/2008.11174/) (2020)
- [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)

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

- [The State of Robot Motion Generation](https://mlsanity.com/paper/2410.12172/) (2024)
- [Foundations of a Developmental Design Paradigm for Integrated Continual Learning, Deliberative Behavior, and Comprehensibility](https://mlsanity.com/paper/2502.13935/) (2025)
- [Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization](https://mlsanity.com/paper/2407.05511/) (2024)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning](https://mlsanity.com/paper/2305.10442/) (2023)
- [Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence](https://mlsanity.com/paper/2506.12891/) (2025)

## Abstract (arXiv)

Sampling-based methods are widely adopted solutions for robot motion planning. The methods are straightforward to implement, effective in practice for many robotic systems. It is often possible to prove that they have desirable properties, such as probabilistic completeness and asymptotic optimality. Nevertheless, they still face challenges as the complexity of the underlying planning problem increases, especially under tight computation time constraints, which impact the quality of returned solutions or given inaccurate models. This has motivated machine learning to improve the computational efficiency and applicability of Sampling-Based Motion Planners (SBMPs). This survey reviews such integrative efforts and aims to provide a classification of the alternative directions that have been explored in the literature. It first discusses how learning has been used to enhance key components of SBMPs, such as node sampling, collision detection, distance or nearest neighbor computation, local planning, and termination conditions. Then, it highlights planners that use learning to adaptively select between different implementations of such primitives in response to the underlying problem's features. It also covers emerging methods, which build complete machine learning pipelines that reflect the traditional structure of SBMPs. It also discusses how machine learning has been used to provide data-driven models of robots, which can then be used by a SBMP. Finally, it provides a comparative discussion of the advantages and disadvantages of the approaches covered, and insights on possible future directions of research. An online version of this survey can be found at: https://prx-kinodynamic.github.io/