TransSeptal Puncture (TSP) is a key step in many minimally invasive cardiac procedures, enabling access to the left atrium by crossing the interatrial septum from the right atrium.The task demands extreme precision, as excessive force may cause cardiac tamponade.Robotic platforms can improve precision and repeatability, but most of the existing systems are designed for training or teleoperation rather than autonomous execution, and do not incorporate adaptive impedance modulation.This work presents a learning-from-demonstration framework based on probabilistic impedance modeling to investigate the feasibility of learning and embedding expert force-regulation strategies for autonomous execution of the TSP under cliniciandefined targets.Specifically, Gaussian Mixture Models, trained on teleoperated demonstrations, capture the relationship between contact force and operator stiffness, enabling real-time modulation of impedance through Gaussian Mixture Regression.The adaptive controller was deployed on a 7-DoF robotic platform and validated on fossa ovalis phantoms of varying compliance and on ex vivo porcine tissue.Performance was evaluated using clinically relevant metrics: Needle Puncture Force (NPF), Tenting Distance (TD), and Needle Stopping Space (NSS).Compared to fixed-stiffness baselines, the proposed controller reduced NPF by up to 35%, maintained TD within safe limits, and limited NSS below 1 mm in nominal anatomies, with consistent performance also observed under extreme phantom anatomies and ex vivo tissue.These findings demonstrate that embedding humanlike impedance modulation can enable safe, anatomy-aware autonomous control, advancing robotic TSP toward clinical feasibility.Note to Practitioners-This study proposes a learning-based adaptive impedance control scheme for TSP, a delicate step in minimally invasive cardiac procedures where excessive puncture forces can lead to severe complications.While robotic systems can enhance precision and repeatability, existing platforms cannot typically autonomously adapt to patient-specific anatomical variability, relying instead on operator input.The proposed approach consists of two main steps.First, impedance profiles are extracted from teleoperated demonstrations, capturing the relationship between applied force and operator stiffness through a probabilistic model.Then, during autonomous execution, the robot leverages this model to modulate its impedance in real time, adapting its behavior to the compliance of the cardiac tissue to perform safe and precise punctures.By embedding human-like impedance modulation into the control loop, this framework combines surgical expertise with robotic precision, improving procedural safety and consistency.Beyond TSP, the same strategy can be applied to other surgical tasks that require adaptation to different patient anatomies, such as vascular access,

This paper proposes a quality-driven adaptive control framework for robotic vascular anatomies scanning to facilitate the acquisition of high-quality ultrasound (US) images. Specifically, a novel probability-based US image quality evaluation metric for vascular anatomies is introduced, leveraging an image segmentation network to establish a mapping between the controlled variables of the robot (e.g., pose and force) and US image quality. Furthermore, an adaptive US probe control strategy driven by US image quality is developed to optimize real-time image acquisition, with its stability rigorously proven. To assess the effectiveness of the proposed framework, two experiments were conducted on a human tissue-mimicking phantom, encompassing both static and dynamic scenarios. The experimental results demonstrate that the proposed framework ensures stable contact force and significantly enhances US image quality for robot-assisted vascular anatomy imaging, even in the presence of external disturbances.

This work presents a compliant and passive shared control framework for teleoperated robot-assisted tasks. Inspired by the human operator's capability of continuously regulating the arm impedance to perform contact-rich tasks, a novel control schema, exploiting the variable impedance control framework for force tracking is proposed. Moreover, bilateral teleoperation and shared control strategies are implemented to alleviate the human operator's workload. Furthermore, a global energy tank-based approach is integrated to enforce the system's passivity. The proposed framework is first evaluated to assess the force-tracking capability when the robot autonomously performs contact-rich tasks, e.g., in an ultrasound scanning scenario. Then, a validation experiment is conducted utilizing the proposed shared control framework. Finally, the system's usability is investigated with 12 users. The experiment results in system assessment revealed a maximum median error of 0.25 N across all the force-tracking experiment setups, i.e., constant and time-varying ones. Then, the validation experiment demonstrated significant improvements regarding the force tracking tasks compared to conventional control methods, and the system passivity was preserved during the task execution. Finally, the usability experiment shows that the human operator workload is significantly reduced by$54.6 \%$compared to the other two control modalities. The proposed framework holds significant potential for the execution of remote robot-assisted medical procedures, such as palpation and ultrasound scanning, particularly in addressing deformation challenges while ensuring safety, compliance, and system passivity.

This paper presents an Augmented Reality (AR)assisted robot learning framework for Minimally Invasive Surgery (MIS) tasks. The proposed framework exploits an external optical tracking system to collect human demonstration. Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) are utilized to encode and generate a robust desired trajectory for transferring to the real robot for the MIS task. The HoloLens 2 Head-Mounted-Display (HMD) is integrated for intuitive visualization of the robot configuration under the constraint of a small incision on the patient's abdominal cavity during the demonstration phase. Experiments are conducted to verify the feasibility and performance of the proposed framework and compared it with the kinesthetic teaching-based modality in a tumor resection MIS task. The results illustrate that the proposed AR-assisted robot learning framework requires lower workload demand, achieves higher performance and efficiency, and ensures the feasibility of the learned results for reproduction on a real robot for MIS tasks.

In this paper, a position-based visual-servoing control approach is introduced for a robotic camera holder to improve ergonomics and reduce mental stress during brain surgery. The visual tracking system controls and moves the robotic camera holder by following a selected surgical instrument without the need for artificial markers. The system was validated using a 7 Degree-of-Freedoms (DoFs) redundant robotic manipulator with an eye-in-hand stereo camera configuration and compared with conventional control methods using NASA TLX survey and four objective metrics, including execution time, time out of field of view (FoV), target score, and path length. Experimental results demonstrate that the proposed system can reduce the surgeon's workload during brain surgery-related task execution, improve ergonomics and achieve higher performance than traditional control methods.

Under-actuated bionic hands have achieved tremendous popularity in many fields because of their advantages of lightweight, budget-friendly, satisfactory flexibility, and adaptability. Except for the bionic mechanical design, various anthropomorphic control strategies have been proposed and investigated in the last decades. However, due to its under-actuated characteristic, there are still many challenges for anthropomorphic control of all the degrees of freedom (DOFs) using less input. It is challenging to map the human hand kinematic synergies on robotic hands, particularly for a dynamic environment. Therefore, it is worth studying how to control the under-actuated bionic hand effectively in a dynamic environment. In this paper, an anthropomorphic control method is proposed using sensor fusion of hand kinematic inputs to control the under-actuated bionic hand. In order to map the kinematics of human fingers to the bionic hand, a novel finger bending angle is defined to represent the posture of human fingers. Multiple Leap Motion Controllers (LMC) are fused to estimate the stable and accurate finger bending angles to avoid the occlusion problem. Finally, experiments with real-time control of the under-actuated bionic hand are implemented to demonstrate the proposed approach’s effectiveness.