Abstract
Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation using the jigsaw game to address the spatial-temporal reasoning skills for robot learning. In particular, our method exploits a simple set of jigsaw pieces by designing a structured protocol following a typical, functional implementation of robot manipulation, which is also highly customizable according to a wide range of task specifications. Researchers interested in understanding the performances of their hardware, software, and integration can selectively adopt the proposed protocol to benchmark their research outputs on a comparable scale in the functional, task and system level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, which is commonly available in other established engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.
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BibTeX (Download)
@workshop{Liu2019Jigsaw,
title = {Jigsaw-based Benchmarking for Learning Robotic Manipulation},
author = {Xiaobo Liu and Fang Wan and Haoran Sun and Qichen Luo and Wei Zhang and Chaoyang Song},
url = {https://www.ycbbenchmarks.com/ICRA2019_workshop},
year = {2019},
date = {2019-05-23},
urldate = {2019-05-23},
abstract = {Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation using the jigsaw game to address the spatial-temporal reasoning skills for robot learning. In particular, our method exploits a simple set of jigsaw pieces by designing a structured protocol following a typical, functional implementation of robot manipulation, which is also highly customizable according to a wide range of task specifications. Researchers interested in understanding the performances of their hardware, software, and integration can selectively adopt the proposed protocol to benchmark their research outputs on a comparable scale in the functional, task and system level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, which is commonly available in other established engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.},
note = {Invited Presentation at ICRA 2019 Workshop on Benchmarks for Robotic Manipulation},
keywords = {ICRA, Workshop},
pubstate = {published},
tppubtype = {workshop}
}
