# Bayesian Local Sampling-based Planning

arXiv: https://arxiv.org/abs/1909.03452 · HTML: https://mlsanity.com/paper/1909.03452/

## Key facts

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

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

- [Parallelised Diffeomorphic Sampling-based Motion Planning](https://mlsanity.com/paper/2108.11775/) (2021)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-based Semantic Scene Understanding](https://mlsanity.com/paper/2209.05222/) (2022)
- [L4KDE: Learning for KinoDynamic Tree Expansion](https://mlsanity.com/paper/2203.00975/) (2022)
- [sbp-env: Sampling-based Motion Planners' Testing Environment](https://mlsanity.com/paper/2110.08402/) (2021)
- [Discover Life Skills for Planning with Bandits via Observing and Learning How the World Works](https://mlsanity.com/paper/2207.08130/) (2022)
- [Transfer Learning for Assessing Heavy Metal Pollution in Seaports Sediments](https://mlsanity.com/paper/2506.22096/) (2025)

## Abstract (arXiv)

Sampling-based planning is the predominant paradigm for motion planning in robotics. Most sampling-based planners use a global random sampling scheme to guarantee probabilistic completeness. However, most schemes are often inefficient as the samples drawn from the global proposal distribution, and do not exploit relevant local structures. Local sampling-based motion planners, on the other hand, take sequential decisions of random walks to samples valid trajectories in configuration space. However, current approaches do not adapt their strategies according to the success and failures of past samples. In this work, we introduce a local sampling-based motion planner with a Bayesian learning scheme for modelling an adaptive sampling proposal distribution. The proposal distribution is sequentially updated based on previous samples, consequently shaping it according to local obstacles and constraints in the configuration space. Thus, through learning from past observed outcomes, we maximise the likelihood of sampling in regions that have a higher probability to form trajectories within narrow passages. We provide the formulation of a sample-efficient distribution, along with theoretical foundation of sequentially updating this distribution. We demonstrate experimentally that by using a Bayesian proposal distribution, a solution is found faster, requiring fewer samples, and without any noticeable performance overhead.