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MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

Constantinos Chamzas, Carlos Quintero-Pena, Zachary Kingston, Andreas Orthey, Daniel Rakita, Michael Gleicher, Marc Toussaint, Lydia E. Kavraki
IEEE Robotics and Automation Letters, Volume 7, Number 2, page 882-889 — 2022
    Download the publication : CQKORGTE22.pdf [7.3Mo]  
    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.

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    BibTex references

    @Article{CQKORGTE22,
      author       = "Chamzas, Constantinos and Quintero-Pena, Carlos and Kingston, Zachary and Orthey, Andreas and Rakita, Daniel and Gleicher, Michael and Toussaint, Marc and E. Kavraki, Lydia",
      title        = "MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets",
      journal      = "IEEE Robotics and Automation Letters",
      number       = "2",
      volume       = "7",
      pages        = "882-889",
      year         = "2022",
      url          = "http://graphics.cs.wisc.edu/Papers/2022/CQKORGTE22"
    }
    
     

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