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Proceedings of the 4th International Conference on Image, Algorithms, and Artificial Intelligence, ICIAAI 2026, 22-24 May 2026, Singapore, Singapore

Research Article

Design of A Robotic Arm Error Compensation System Based on the Transformer Model

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  • @INPROCEEDINGS{10.4108/eai.22-5-2026.2365357,
        author={Yuqian  Li},
        title={Design of A Robotic Arm Error Compensation System Based on the Transformer Model},
        proceedings={Proceedings of the 4th International Conference on Image, Algorithms, and Artificial Intelligence, ICIAAI 2026, 22-24 May 2026, Singapore, Singapore},
        publisher={EAI},
        proceedings_a={ICIAAI},
        year={2026},
        month={8},
        keywords={Robotic arms pose compensation Transformer Industrial robots},
        doi={10.4108/eai.22-5-2026.2365357}
    }
    
  • Yuqian Li
    Year: 2026
    Design of A Robotic Arm Error Compensation System Based on the Transformer Model
    ICIAAI
    EAI
    DOI: 10.4108/eai.22-5-2026.2365357
Yuqian Li1,*
  • 1: College of Computer Science, Beijing University of Technology, Beijing, China
*Contact email: 22074804@emails.bjut.edu.cn

Abstract

Industrial robots, especially robotic arms, are widely used in the industrial field today. In view of the end-effector posture error that exists in the robotic arm during the movement process, this paper takes the UR5 robotic arm as the research object and studies the error compensation method based on the Transformer model. By utilizing the temporal information in the dataset, a multi-feature temporal model compensation method based on the Transformer is proposed. Experimental results show that the proposed method can effectively predict the end-effector pose errors of the robotic arm. Compared with the baseline model, it achieves better prediction performance, demonstrating the effectiveness of the Transformer-based method in robotic arm pose error modeling.

Keywords
Robotic arms pose compensation, Transformer, Industrial robots
Published
2026-08-31
Publisher
EAI
http://dx.doi.org/10.4108/eai.22-5-2026.2365357
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