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Generalizable and precise control based on equilibrium-point hypothesis for musculoskeletal robotic system

Yaxiong Wu (The School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China)
Jiahao Chen (The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China)
Hong Qiao (The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China and The Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China)

Robotic Intelligence and Automation

ISSN: 2754-6969

Article publication date: 11 June 2024

Issue publication date: 18 July 2024

62

Abstract

Purpose

The purpose of this study is realizing human-like motions and performance through musculoskeletal robots and brain-inspired controllers. Human-inspired robotic systems, owing to their potential advantages in terms of flexibility, robustness and generality, have been widely recognized as a promising direction of next-generation robots.

Design/methodology/approach

In this paper, a deep forward neural network (DFNN) controller was proposed inspired by the neural mechanisms of equilibrium-point hypothesis (EPH) and musculoskeletal dynamics.

Findings

First, the neural mechanism of EPH in human was analyzed, providing the basis for the control scheme of the proposed method. Second, the effectiveness of proposed method was verified by demonstrating that equilibrium states can be reached under the constant activation signals. Finally, the performance was quantified according to the experimental results.

Originality/value

Based on the neural mechanism of EPH, a DFNN was crafted to simulate the process of activation signal generation in human motion control. Subsequently, a bio-inspired musculoskeletal robotic system was designed, and the high-precision target-reaching tasks were realized in human manner. The proposed methods provide a direction to realize the human-like motion in musculoskeletal robots.

Keywords

Acknowledgements

This work is supported partly by Major Project of Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence (Grant No. 2021ZD0200408), partly by the National Natural Science Foundation of China (NSFC) (under Grants 91948303, 62203439, 62203443).

Citation

Wu, Y., Chen, J. and Qiao, H. (2024), "Generalizable and precise control based on equilibrium-point hypothesis for musculoskeletal robotic system", Robotic Intelligence and Automation, Vol. 44 No. 4, pp. 570-578. https://doi.org/10.1108/RIA-01-2024-0022

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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