학술논문

Evolutionary Search of Optimal Hyperparameters for Learning Various Robot Manipulation Tasks
Document Type
Conference
Source
2024 IEEE Congress on Evolutionary Computation (CEC) Evolutionary Computation (CEC), 2024 IEEE Congress on. :1-8 Jun, 2024
Subject
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Heuristic algorithms
Stacking
Evolutionary computation
Search problems
Trajectory
Planning
Task analysis
Dynamic movement primitives
evolutionary algorithm
robot manipulator
optimal learning
Language
Abstract
This paper presents a comprehensive study of robotic manipulation tasks, focusing on the movement planning and task-handling capabilities of robots. We introduce a novel approach that employs Dynamic Movement Primitives (DMP) for movement planning, coupled with an evolutionary algorithm, specifically the Genetic Algorithm (GA), for hyperparameter tuning of the DMP. Our method significantly enhances the system's precision and control, thereby facilitating a more accurate output. Furthermore, we conduct a comparative analysis of two optimization techniques - user-based and GA-based. Our findings indicate that the GA-based technique offers superior precision, underscoring its potential in advancing robotic manipulation tasks.