Supporting files for thesis "Deep-learning-based Morphological Modelling: Case Study in Soft Robot Control, Shape Sensing and Deformation"

The main focus of this thesis is to develop appropriate morphology modelling strategies for typical deformable structures by integrating physics-aligned prior knowledge and deep learning algorithms. To perform accurate control for soft continuum robots, a reinforcement-learning (RL)-based framework is proposed, which integrates conventional piecewise curvature constant (PCC) model and adaptive learning strategies. The algorithm of deep deterministic policy gradient (DDPG) along with domain randomization and offline retraining facilitates fast initialization and stable path following, even under varying tip load, demonstrating its advantages over Jacobian model-based and supervised-learning-based control methods. Not only tube-like soft robots, this thesis also endeavors to enhance the proprioception capabilities of soft flexible surfaces. A real-time shape sensing framework is proposed that combines finite element analysis (FEA) with autoregressive deep learning, requiring only sparsely distributed sensor nodes to predict continuous high-order complex 3D shapes. The cost-effective optical waveguide is utilized to detect the light loss induced by surface deformation. While sharing similarities with thin surfaces in terms of structure, cloth presents unique challenges due to its hyper flexibility and lack of adhesive sensing capabilities. To achieve real-time shape prediction of the cloth when in contact with human bodies or obstacles, a framework based on graph-neural-networks (GNNs) is introduced. Leveraging graph structure and spiral graph convolution, this approach facilitates efficient processing of high-dimensional cloth data while preserving essential geometry information. Additionally, a real fabric motion capture system is developed to establish a real 3D cloth library, aiding research on fabric deformation. Considering 2D images are more accessible and common compared to 3D topology data, this thesis also investigates soft tissue modelling via medical images. To explore the hidden tissue properties through ultrasound B-mode images, a framework that incorporates U-Net and ultrasound propagation physics is proposed. The framework can estimate physics-aligned tissue properties which characterize ultrasound attenuation, reflection and backscattering, thereby showing potential for enhancing soft tissue deformation modelling.