AccScience Publishing / IJAMD / Online First / DOI: 10.36922/ijamd.2328

Joint angle prediction for a cable-driven gripper with variable joint stiffness through numerical modeling and machine learning

Guo Liang Goh1 Xi Huang1 William Toh1 Zhengchen Li1 Samuel Lee1 Van Pho Nguyen1,2 Wai Yee Yeong1* Boon Siew Han2 Teng Yong Ng1
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1 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Republic of Singapore
2 Schaeffler Hub for Advanced Research, Nanyang Technological University, Singapore, Republic of Singapore
IJAMD 2024, 1(1), 62–74;
Submitted: 28 November 2023 | Accepted: 10 January 2024 | Published: 29 January 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

Soft grippers in automation, particularly those with variable joint stiffness, offer promising possibilities for precise manipulation tasks. However, accurately predicting finger joint bending angles in this field poses significant challenges due to the soft and complex nature of the grippers, making modeling and angle prediction difficult. This paper presents the development of a predictive model for precisely controlling bending angles in multi-material printed soft grippers with variable stiffness, which are pivotal for delicate manipulation tasks in automation. In particular, we explore a cable-driven gripper design made of thermoplastic polyurethane and conductive polylactic acid materials, featuring integrated resistive joints for stiffness modulation through controlled Joule heating. A data-driven modeling approach, combining numerical modeling of the gripper and machine learning techniques, was employed for the development of the predictive model. We performed static structural simulations using ANSYS Workbench to measure bending angles under various conditions for developing datasets for model training. In this work, we evaluated several machine learning models such as linear regression, decision tree, and K-nearest neighbor regression models to predict the correlation between temperature, pull distance, and bending angle. The K-nearest neighbor regression model demonstrated the highest accuracy, with a mean absolute error of approximately 11%. These findings underline the importance of precise angle prediction models in enhancing the functionality and reliability of soft grippers, paving the way for their broader application in automation and robotics.

3D printing
Variable stiffness gripper
Soft robotics
Machine learning
Numerical modelling
Agency for Science, Technology, and Research (A*STAR)
Schaeffler Hub for Advanced Research at NTU
National Research Foundation, Prime Minister’s Office, Singapore
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Conflict of interest
The authors declare that they have no competing interests.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Published by AccScience Publishing