Design and Implementation of Bioinspired Soft Robotic Systems for Adaptive Interaction in Unstructured Human Environments

Abstract

Robotic systems designed for human-centered applications increasingly operate in unstructured and dynamically changing environments, where rigid-bodied robots face limitations in adaptability, safety, and physical compliance. Bioinspired soft robotics has emerged as a promising paradigm that draws inspiration from biological organisms to achieve flexible, resilient, and adaptive interaction capabilities. This paper presents the design, fabrication, control, and experimental validation of a bioinspired soft robotic system intended for adaptive interaction in unstructured human environments. The proposed system integrates elastomeric actuators, distributed soft sensors, and morphology-driven intelligence to enable safe physical interaction and environmental adaptability. A hybrid control framework combining model-based control and machine learning techniques is developed to manage nonlinear material behavior and uncertain external conditions. Experimental evaluations demonstrate the robot’s ability to conform to irregular objects, adapt locomotion strategies, and maintain stable interaction with human users. Performance metrics including compliance, response time, adaptability, and interaction safety are analyzed and compared with conventional rigid robotic systems. The results confirm that bioinspired soft robotic architectures significantly enhance adaptability and safety in complex environments. This research contributes to the advancement of next-generation robotic systems for healthcare, service robotics, and human–robot collaboration.