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, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers 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, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.
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BibTeX (Download)
@conference{Liu2023JigsawBased,
title = {Jigsaw-based Benchmarking for Learning Robotic Manipulation},
author = {Xiaobo Liu and Fang Wan and Sheng Ge and Haokun Wang and Haoran Sun and Chaoyang Song},
url = {http://www.ieee-arm.org/icarm2023/},
doi = {10.1109/ICARM58088.2023.10218784},
year = {2023},
date = {2023-07-08},
urldate = {2023-07-08},
booktitle = {IEEE International Conference on Advanced Robotics and Mechatronics (ICARM2023)},
address = {Sanya, China},
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, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers 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, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.},
keywords = {Best Conference Paper Finalist, Corresponding Author, ICARM, Paper Award},
pubstate = {published},
tppubtype = {conference}
}
