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

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.

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