Jigsaw-Based Benchmarking for Learning Robotic Manipulation

Xiaobo Liu1; Fang Wan1; Sheng Ge1; Haokun Wang2; Haoran Sun1; Chaoyang Song1

10.1109/ICARM58088.2023.10218784

2023

IEEE International Conference on Advanced Robotics and Mechatronics (ICARM).

979-8-3503-0018-5

2023 International Conference on Advanced Robotics and Mechatronics (ICARM)

124-130

8-10 July 2023

Sanya, China

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.

Three-dimensional Displays Protocols Systematics System Performance Benchmark Testing Robot Learning Planning

第一

EI

20233814760504

Robotics ; Table lookup

Computer Programming:723.1 ; Robotics:731.5

IEEE

1.Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
2.Robotics & Autonomous Systems Thrust, System Hub, Hong Kong University of Science and Technology, Hong Kong, China

Xiaobo Liu,Fang Wan,Sheng Ge,et al. Jigsaw-Based Benchmarking for Learning Robotic Manipulation[C],2023:124-130.