Recent Advances in Modeling, Control and Motion Planning for AI-Driven Soft Robotic Systems

Abstract

Due to their high compliance, environmental adaptability, and inherent safety in human–robot interaction, soft robots have demonstrated considerable potential, including medical rehabilitation, compliant grasping, and operations in complex environments. Unlike conventional robots relying on rigid structures and discrete joint motions, soft robots use flexible materials such as elastomers and hydrogels to achieve motion and functionality through continuous deformation. Despite providing excellent adaptability, these compliant materials also introduce complex behaviors, including continuous morphological changes, material nonlinearities, time-varying properties, and environmental coupling, posing substantial challenges to modeling, control, and motion planning. In recent years, artificial intelligence (AI), through nonlinear representation, data-driven learning, adaptive optimization, and autonomous decision-making, has provided new technical pathways for addressing these challenges. Unlike previous reviews organized primarily by AI algorithm type, this work narratively reviews 39 studies and organizes AI-driven approaches according to the soft robotic system implementation process, encompassing modeling, control, and motion planning. Specifically, in modeling, neural networks, Koopman representations, and physics–data fusion methods have enhanced the representation of complex dynamical relationships. In control, learning-augmented control and reinforcement learning have reduced reliance on accurate system models and improved adaptability under uncertainty. In motion planning, learning from demonstration, reinforcement learning, and intelligent optimization methods have strengthened the capability for autonomous motion generation under complex constraints. Finally, this paper summarizes the current state of the field and discusses future trends in AI-driven soft robotics, emphasizing stronger coordination across functional modules. Closed-loop integration of prediction, decision-making, control, and feedback updates may further advance soft robots from stage-specific intelligence toward system-level autonomy.