Offline-Trained GAN-Augmented Highly Adaptive Control with Multi-DoF Fusion for Pneumatic Soft Surgical Robots (I)

Abstract

Pneumatic soft robots are well-suited for minimally invasive surgery owing to their compliance and safe interaction with tissues. However, achieving highly adaptive control is difficult owing to modeling inaccuracies, inter-chamber coupling, and disturbances from surgical instruments. Non-learning adaptive methods depend on simplified models and perform poorly in unstructured settings. Conversely, learning-based methods often impose high computational costs in multi-degree-of-freedom (multi-DoF) pneumatic systems. A previous study proposed a generative adversarial network (GAN)-based proportional–integral–derivative (G-PID) controller that combined PID stability with learning-based adaptability by aligning system behavior with a reference model. However, its performance in highly coupled multi-DoF pneumatic soft robots was unverified, and its online adversarial training was computationally intensive. We addressed these limitations by developing an offline-trained G-PID controller, shifting adversarial training offline to reduce computational overhead, achieving 23-fold faster convergence, and enabling real-time, model-free control with balanced adaptability and efficiency. We evaluated three multi-DoF data fusion strategies, showing effective coordination of DoF coupling while maintaining individual control fidelity. Validation on a multi-DoF soft robotic mechatronic system for single-port transvesical prostatectomy revealed tip errors below 0.16 mm across surgical instrument