MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

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...

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Vydáno v:IEEE robotics and automation letters Ročník 7; číslo 2; s. 882 - 889
Hlavní autoři: Chamzas, Constantinos, Quintero-Pena, Carlos, Kingston, Zachary, Orthey, Andreas, Rakita, Daniel, Gleicher, Michael, Toussaint, Marc, Kavraki, Lydia E.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Benchmark testing
Benchmarks
Data sets for robot learning
Datasets
Feasibility
Generators
manipulation planning
motion and path planning
Motion planning
Planning
Prefabrication
Robot sensing systems
Robots
Task analysis
ISSN:2377-3766, 2377-3766
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3133603