Knowledge of the tip contact force in continuum robots, which are often used as medical instruments, is critical for clinical applications. It enhances the interventionalist's decision-making, navigation efficiency, and procedural safety. However, accurately determining the tip contact force in conventionally sized instruments remains challenging. This study introduces a learning-based method for estimating the external contact force at the tip of a continuum robot. By leveraging curvature and bending angle data from a multi-core fiber equipped with fiber Bragg gratings (FBGs) embedded inside the Nitinol tube, the method maps these inputs to the corresponding tip force in 3D. Experiments conducted on an FBG-embedded Nitinol rod validate the feasibility of the proposed method, yielding Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) values of 20.9$\left(m N^{2}\right), 2.7(m N)$, and$4.6(m N)$, respectively, which represent a 26 % improvement compared to the learning-based vision methodology.

Concentric tube robots (CTRs) hold great potential for minimally invasive surgery, offering flexibility, small diameters, and the ability to navigate within complex anatomical structures. While machine learning models have been increasingly used to predict the kinematics of CTRs, there is a lack of an established framework for evaluating generative inverse kinematic models, which are able to solve the inverse kinematic problem by providing various joint solutions for a desired end position. In this study, we introduce a workspace-based measure to assess the diversity of solutions produced by three generative models: an invertible neural network (INN), a conditional invertible neural network (cINN), and a conditional variational autoencoder (cVAE). We find that all three models record similar end position errors (3-6 mm) on dexterous subsets of the workspace, but that a cINN outperforms the others in generating diverse solutions using a workspace-based 1-Wasserstein distance by at least 2.38 standard deviations. To further test the applicability of these models, we integrate the best-performing cINN into a CTR controller and demonstrate the first use of a generative CTR model with real-time teleoperation under task-based constraints.

Open Spina Bifida (OSB) is a congenital neural tube defect where a major component of the procedure to repair the defect involves the closure of a lesion wound through suturing. For a minimally invasive approach, tools entering the uterus to access the fetus should be as thin as possible to minimize maternal risk. This work presents the design of a 3 degrees-of-freedom, 2mm diameter tool wrist with a bending range of motion from 0° to 90°. This wrist is capable of generating up to 2N of force measured from the end of the wrist and achieving a bending curvature of 107m-1(9.35mm bending radius). A pseudo-rigid body kinematic model has been implemented for the control of this tool with a protocol for singularity mitigation and avoidance. Timed teleoperation studies explicitly demonstrate that the tool is able to reliably execute suturing with a fastest achieved time of under 3 minutes for a simple interrupted suturing technique.