High-Precise Robot Arm Manipulation based on Online Iterative Learning and Forward Simulation with Positioning Error Below End-Effector Physical Minimum Displacement
Qu Weiming, Liu Tianlin, Luo Dingsheng

TL;DR
This paper introduces a novel high-precision robot arm manipulation framework combining online iterative learning and forward simulation, achieving sub-minimum displacement accuracy and demonstrating effectiveness on real and simulated platforms.
Contribution
It presents a new framework that integrates online iterative learning with forward simulation for high-precision manipulation, considering joint resolution, and parallelizes strategies for improved accuracy.
Findings
Achieves positioning error below end-effector physical minimum displacement.
Demonstrates effectiveness on both simulation and real UR3 robot platforms.
Parallelizes multiple high-precision strategies for enhanced performance.
Abstract
Precision is a crucial performance indicator for robot arms, as high precision manipulation allows for a wider range of applications. Traditional methods for improving robot arm precision rely on error compensation. However, these methods are often not robust and lack adaptability. Learning-based methods offer greater flexibility and adaptability, while current researches show that they often fall short in achieving high precision and struggle to handle many scenarios requiring high precision. In this paper, we propose a novel high-precision robot arm manipulation framework based on online iterative learning and forward simulation, which can achieve positioning error (precision) less than end-effector physical minimum displacement. Additionally, we parallelize multiple high-precision manipulation strategies to better combine online iterative learning and forward simulation. Furthermore,…
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Taxonomy
TopicsRobotic Mechanisms and Dynamics · Robot Manipulation and Learning · Soft Robotics and Applications
