MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets
arXiv:2112.06402 · doi:10.1109/LRA.2021.3133603
Abstract
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.
arXiv commentaccepted in IEEE Robotics and Automation Letters (RAL), 2022. Supplementary video: https://youtu.be/t96Py0QX0NI Code: https://github.com/KavrakiLab/motion_bench_maker
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Cited by in corpus (8)
- A Survey on the Integration of Machine Learning with Sampling-based Motion Planning
- Global Tensor Motion Planning
- Smart placement, faster robots-a comparison of algorithms for robot base-pose optimization
- Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees (FCIT*)
- Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities
- A Framework for Joint Grasp and Motion Planning in Confined Spaces
- M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes
- Certification of Bottleneck Task Assignment with Shortest Path Criteria