# sbp-env: Sampling-based Motion Planners' Testing Environment

arXiv: https://arxiv.org/abs/2110.08402 · HTML: https://mlsanity.com/paper/2110.08402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics](https://mlsanity.com/paper/2002.06599/) (2020)
- [PythonRobotics: a Python code collection of robotics algorithms](https://mlsanity.com/paper/1808.10703/) (2018)
- [Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees](https://mlsanity.com/paper/1810.03749/) (2018)
- [Bayesian Local Sampling-based Planning](https://mlsanity.com/paper/1909.03452/) (2019)
- [Learning to Plan Optimally with Flow-based Motion Planner](https://mlsanity.com/paper/2010.11323/) (2020)
- [Rapidly-exploring Random Forest: Adaptively Exploits Local Structure with Generalised Multi-Trees Motion Planning](https://mlsanity.com/paper/2103.04487/) (2021)
- [Cooperative Pathfinding based on memory-efficient Multi-agent RRT*](https://mlsanity.com/paper/1911.03927/) (2019)
- [Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph](https://mlsanity.com/paper/2109.10209/) (2021)

## Built on by (most cited first, 1 of 1)

- [Discovering Multiple Algorithm Configurations](https://mlsanity.com/paper/2303.07434/) (2023)

## Abstract (arXiv)

Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for motion planning. sbp-env focuses on the flexibility of tinkering with different aspects of the framework, and had divided the main planning components into two categories (i) samplers and (ii) planners. The focus of motion planning research had been mainly on (i) improving the sampling efficiency (with methods such as heuristic or learned distribution) and (ii) the algorithmic aspect of the planner using different routines to build a connected graph. Therefore, by separating the two components one can quickly swap out different components to test novel ideas.