Jigsaw-based Benchmarking for Learning Robotic Manipulation
| Autor: | Liu, Xiaobo, Wan, Fang, Ge, Sheng, Wang, Haokun, Sun, Haoran, Song, Chaoyang |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 7 pages, 7 figures, accepted to 2023 IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) |
| Databáze: | arXiv |
| Externí odkaz: |
|