# MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

arXiv: https://arxiv.org/abs/2112.06402 · HTML: https://mlsanity.com/paper/2112.06402/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2021.

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

- [Evaluating Real-World Robot Manipulation Policies in Simulation](https://mlsanity.com/paper/2405.05941/) (2024)
- [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023)
- [ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models](https://mlsanity.com/paper/2409.15250/) (2024)
- [Neural MP: A Generalist Neural Motion Planner](https://mlsanity.com/paper/2409.05864/) (2024)
- [CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation](https://mlsanity.com/paper/2304.09302/) (2023)
- [RobotArena ∞: Scalable Robot Benchmarking via Real-to-Sim Translation](https://mlsanity.com/paper/2510.23571/) (2025)
- [Instructing Robots by Sketching: Learning from Demonstration via Probabilistic Diagrammatic Teaching](https://mlsanity.com/paper/2309.03835/) (2023)
- [A Survey on the Integration of Machine Learning with Sampling-based Motion Planning](https://mlsanity.com/paper/2211.08368/) (2022)
- [M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.06678/) (2024)
- [Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks](https://mlsanity.com/paper/2211.06134/) (2022)
- [Path Signatures for Diversity in Probabilistic Trajectory Optimisation](https://mlsanity.com/paper/2308.04071/) (2023)
- [Global Tensor Motion Planning](https://mlsanity.com/paper/2411.19393/) (2024)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [The Planner Optimization Problem: Formulations and Frameworks](https://mlsanity.com/paper/2303.06768/) (2023)
- [RoboArm-NMP: a Learning Environment for Neural Motion Planning](https://mlsanity.com/paper/2405.16335/) (2024)
- [Multi Graph Search for High-Dimensional Robot Motion Planning](https://mlsanity.com/paper/2602.12096/) (2026)

## Abstract (arXiv)

Recently, there has been a wealth of development in motion planning for robotic manipulation new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker, an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground to accelerate motion planning research.