# Learning Implicit Sampling Distributions for Motion Planning

arXiv: https://arxiv.org/abs/1806.01968 · HTML: https://mlsanity.com/paper/1806.01968/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 16 of 16)

- [Motion Policy Networks](https://mlsanity.com/paper/2210.12209/) (2022)
- [Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks](https://mlsanity.com/paper/2210.08864/) (2022)
- [Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projection](https://mlsanity.com/paper/2205.04667/) (2022)
- [PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation](https://mlsanity.com/paper/2409.16012/) (2024)
- [Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks](https://mlsanity.com/paper/2301.04330/) (2023)
- [Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments](https://mlsanity.com/paper/2509.06953/) (2025)
- [Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning](https://mlsanity.com/paper/2109.14325/) (2021)
- [Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation](https://mlsanity.com/paper/2204.08647/) (2022)
- [Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding](https://mlsanity.com/paper/2210.08408/) (2022)
- [Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller](https://mlsanity.com/paper/2404.01184/) (2024)
- [Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning](https://mlsanity.com/paper/2306.06754/) (2023)
- [Warm-Starting Optimization-Based Motion Planning for Robotic Manipulators via Point Cloud-Conditioned Flow Matching](https://mlsanity.com/paper/2510.03460/) (2025)
- [Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning](https://mlsanity.com/paper/2202.00907/) (2022)
- [Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning](https://mlsanity.com/paper/2204.06791/) (2022)
- [Visualizing High-Dimensional Configuration Spaces: A Comprehensive Analytical Approach](https://mlsanity.com/paper/2312.10918/) (2023)
- [Fast End-to-End Generation of Belief Space Paths for Minimum Sensing Navigation](https://mlsanity.com/paper/2409.12902/) (2024)

## Abstract (arXiv)

Sampling-based motion planners have experienced much success due to their ability to efficiently and evenly explore the state space. However, for many tasks, it may be more efficient to not uniformly explore the state space, especially when there is prior information about its structure. Previous methods have attempted to modify the sampling distribution using hand selected heuristics that can work well for specific environments but not universally. In this paper, a policy- search based method is presented as an adaptive way to learn implicit sampling distributions for different environments. It utilizes information from past searches in similar environments to generate better distributions in novel environments, thus reducing overall computational cost. Our method can be incor- porated with a variety of sampling-based planners to improve performance. Our approach is validated on a number of tasks, including a 7DOF robot arm, showing marked improvement in number of collision checks as well as number of nodes expanded compared with baseline methods.