Bio-inspired control strategies in wearable robotics: A comprehensive review of CPGs and DMPs

Abstract

Wearable robotic devices such as exoskeletons and orthoses have undergone significant advancements over the past two decades, aiming to support human mobility in rehabilitation, daily life, and industrial settings. Central to their effectiveness is the implementation of control strategies that generate smooth, adaptive, and user-synchronized movements. Among these, bio-inspired approaches that emulate neural and motor mechanisms of human locomotion have gained increasing attention.

This review presents a comprehensive analysis of two prominent bio-inspired control frameworks – Central Pattern Generators (CPGs) and Dynamic Movement Primitives (DMPs) – implemented in wearable lower-limb robotic systems. A total of 45 articles were systematically analysed to identify trends and challenges in their application.

The review examines the purposes of these controllers, the joints and degrees of freedom addressed, the sensors employed, the structural characteristics of each approach, the integration of sensory feedback and intention decoding, the tracking controllers used, and the validation methodologies adopted.

The findings reveal that CPGs and DMPs are primarily adopted for generating adaptive joint trajectories, enabling stable, rhythmic, and responsive locomotion. Their flexibility allows for encoding motion patterns that adapt to user-specific and task-specific requirements. However, challenges such as parameter tuning, integration of sensory feedback, real-time intention decoding, and validation robustness remain open issues.

This work highlights the potential of CPG- and DMP-based strategies to enhance the autonomy, safety, and personalization of wearable robots and provides future research directions to address their current limitations and improve their practical applicability.