A Preliminary Study of Multi-task MAP-Elites with Knowledge Transfer for Robotic Arm Design

The structure design of robotic arms is of great importance on completing industrial tasks successfully. This is a typical multi-task optimization problem when considering different constraints as different tasks. However, mainstream methods for multi-task optimization such as evolutionary multitask...

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Bibliographic Details
Published in:2022 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 8
Main Authors: Liu, Peng, Guo, Zheng, Yu, Hua, Linghu, Han, Li, Yijing, Hou, Yaqing, Ge, Hongwei, Zhang, Qiang
Format: Conference Proceeding
Language:English
Published: IEEE 18.07.2022
Subjects:
Complexity theory
Computational efficiency
Evolutionary computation
Knowledge Transfer
Manipulators
MFEA
Multi-task Optimization
Multitasking
QD algorithm
Robotic Arm Design
Service robots
Task analysis
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Summary:The structure design of robotic arms is of great importance on completing industrial tasks successfully. This is a typical multi-task optimization problem when considering different constraints as different tasks. However, mainstream methods for multi-task optimization such as evolutionary multitasking and Multi-task MAP-Elites algorithms tend to encounter problems such as high computational cost and slow convergence when solving large-scale robotic arm tasks. To this end, this paper proposes a new framework based on the MAP-Elites algorithms for solving large-scale robot arm design tasks, called Multi-task MAP-Elites with Knowledge Transfer (MMKT). Specifically, this paper designs the group-based knowledge transfer process for large-scale task optimization in which all tasks are classified into different groups according to their similarity to generate multiple knowledge transfer areas; and knowledge transfer strategies are designed to enhance the quality of solutions with low fitness value. We test the effectiveness of the MMKT framework in planar robotic arm experiments (2000, 5000, and 10,000 tasks; 10, 15-dimensional search space). The experimental results prove that the MMKT outperforms the MME, CMA-ES, and classical ES algorithms.
DOI:10.1109/CEC55065.2022.9870374