Tendon-sheath mechanisms (TSMs) are widely used in minimally invasive surgical (MIS) applications, but their inherent hysteresis—caused by friction, backlash, and tendon elongation—leads to significant tracking errors. Conventional modeling and compensation methods struggle with these non-linearities and require extensive parameter tuning. To address this, we propose a vibration-assisted hysteresis compensation approach, where controlled vibrational motion is applied along the tendon’s movement direction to mitigate friction and reduce dead zones. Experimental results demonstrate that the exerted vibration consistently reduces hysteresis across all tested frequencies, decreasing RMSE by up to 23.41% (from 2.2345 mm to 1.7113 mm) and improving correlation, leading to more accurate trajectory tracking. When combined with a Temporal Convolutional Network (TCN)-based compensation model, vibration further enhances performance, achieving an 85.2% reduction in MAE (from 1.334 mm to 0.1969 mm). Without vibration, the TCN-based approach still reduces MAE by 72.3% (from 1.334 mm to 0.370 mm) under the same parameter settings. These findings confirm that vibration effectively mitigates hysteresis, improving trajectory accuracy and enabling more efficient compensation models with fewer trainable parameters. This approach provides a scalable and practical solution for TSM-based robotic applications, particularly in MIS.

In robot-assisted minimally invasive surgery (RAMIS), optimal placement of the surgical robot base is crucial for successful surgery. Improper placement can hinder performance because of manipulator limitations and inaccessible workspaces. Conventional base placement relies on the experience of trained medical staff. This study proposes a novel method for determining the optimal base pose based on the surgeon’s working pattern. The proposed method analyzes recorded end-effector poses using a machine learning-based clustering technique to identify key positions and orientations preferred by the surgeon. We introduce two scoring metrics to address the joint limit and singularity issues: joint margin and manipulability scores. We then train a multi-layer perceptron regressor to predict the optimal base pose based on these scores. Evaluation in a simulated environment using the da Vinci Research Kit shows unique base pose score maps for four volunteers, highlighting the individuality of the working patterns. Results comparing with 20,000 randomly selected base poses suggest that the score obtained using the proposed method is 28.2% higher than that obtained by random base placement. These results emphasize the need for operator-specific optimization during base placement in RAMIS.

Peg transfer is a well-known surgical training task in the Fundamentals of Laparoscopic Surgery (FLS). While human surgeons teleoperate robots such as the da Vinci to perform this task with high speed and accuracy, it is challenging to automate. This paper presents a novel system and control method using a da Vinci Research Kit (dVRK) surgical robot and a Zivid depth sensor, and a human subjects study comparing performance on three variants of the peg-transfer task: unilateral, bilateral without handovers, and bilateral with handovers. The system combines 3D printing, depth sensing, and deep learning for calibration with a new analytic inverse kinematics model and time-minimized motion controller. In a controlled study of 3384 peg transfer trials performed by the system, an expert surgical resident, and 9 volunteers, results suggest that the system achieves accuracy on par with the experienced surgical resident and is significantly faster and more consistent than the surgical resident and volunteers. The system also exhibits the highest consistency and lowest collision rate. To our knowledge, this is the first autonomous system to achieve “superhuman” performance on a standardized surgical task. All data is available athttps://sites.google.com/view/surgicalpegtransferNote to Practitioners—This paper presents a new approach to calibrating cable-driven robots based on a combination of 3D printing, depth sensing, inverse kinematics, convex optimization, and deep learning. The approach is applied to calibrating the da Vinci, commercial surgical-assist robot, to automate a standard “pick and place” task. Experiments suggest that the resulting system matches human surgical expert performance in speed and accuracy and significantly outperforms humans in terms of consistency. All details on the system including CAD models, code, and user study data are available online.

Robotic Surgical Assistants (RSAs) are commonly used to perform minimally invasive surgeries by expert surgeons. However, long procedures filled with tedious and repetitive tasks such as suturing can lead to surgeon fatigue, motivating the automation of suturing. As visual tracking of a thin reflective needle is extremely challenging, prior work has modified the needle with nonreflective contrasting paint. As a step towards automation of a suturing subtask without modifying the needle, we propose HOUSTON: Handover of Unmodified, Surgical, Tool-Obstructed Needles, a problem and algorithm that uses a learned active sensing policy with a stereo camera to iteratively localize and align the needle into a visible and accessible pose for the other gripper. To compensate for robot positioning and needle perception errors, the algorithm then executes a high-precision grasping motion that uses multiple cameras. Physical experiments with the da Vinci Research Kit (dVRK) suggest a success rate of 96.7% on needles used in training, and 75 - 92.9% on needles unseen in training. On sequential handovers, HOUSTON successfully executes 32.4 handovers on average before failure. To our knowledge, this work is the first to study handover of unmodified surgical needles. See https: / /tinyurl. com/houston-surgery for additional materials including details about offline datasets and model architectures.

Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics. There has been significant prior work on learning policies for specific fabric manipulation tasks, but comparatively less focus on algorithms which can perform many different tasks. We take a step towards this goal by learning point-pair correspondences across different fabric configurations in simulation. Then, given a single demonstration of a new task from an initial fabric configuration, these correspondences can be used to compute geometrically equivalent actions in a new fabric configuration. This makes it possible to define policies to robustly imitate a broad set of multi-step fabric smoothing and folding tasks. The resulting policies achieve 80.3% average task success rate across 10 fabric manipulation tasks on two different physical robotic systems. Results also suggest robustness to fabrics of various colors, sizes, and shapes. See https://tinyurl.com/fabric-descriptors for supplementary material and videos.

Assisting surgeons with automation of surgical subtasks is challenging due to backlash, hysteresis, and variable tensioning in cable-driven robots. These issues are exacerbated as surgical instruments are changed during an operation. In this work, we propose a framework for automation of high- precision surgical subtasks by learning local, sample-efficient, accurate, closed-loop policies that use visual feedback instead of robot encoder estimates. This framework, which we call deep Intermittent Visual Servoing (IVS), switches to a learned visual servo policy for high-precision segments of repetitive surgical tasks while relying on a coarse open-loop policy for the segments where precision is not necessary. We train the policy using only 180 human demonstrations that are roughly 2 seconds each. Results on a da Vinci Research Kit suggest that combining the coarse policy with half a second of corrections from the learned policy during each high-precision segment improves the success rate on the Fundamentals of Laparoscopic Surgery peg transfer task from 72.9% to 99.2%, 31.3% to 99.2%, and 47.2% to 100.0% for 3 instruments with differing cable properties. In the contexts we studied, IVS attains the highest published success rates for automated surgical peg transfer and is significantly more reliable than previous techniques when instruments are changed. Supplementary material is available at https://tinyurl.com/ivs-icra.

Many surgical robots with steerable surgical instruments have been proposed for endoscopic surgery. Surgical instruments should be small in size for insertion into the body and be able to handle large payloads such as tissue. Because the overall diameter and payload parameters are a trade-off, it is difficult to design an instrument with a large payload while reducing its diameter. In this paper, we optimize the payload of a rolling joint mechanism by deriving the moment equilibrium equation and constraints for endoscopic surgery. A scaled-up prototype was fabricated with the design variables obtained from the optimization, and the validity of the method for calculating the payload was confirmed by the experimentally measured payload. By plotting the distribution of payloads obtained from the moment equilibrium equation, we also confirmed that the payload obtained from the optimization is the maximum. In addition, optimizations with different numbers of joints confirm that the payload tends to decrease as the number of joints increases. This payload optimization method could also be extended to minimizing the deflection of the bending section against external forces and minimizing the diameter of the surgical instrument given the minimum required payload.

Sequential pulling policies to flatten and smooth fabrics have applications from surgery to manufacturing to home tasks such as bed making and folding clothes. Due to the complexity of fabric states and dynamics, we apply deep imitation learning to learn policies that, given color (RGB), depth (D), or combined color-depth (RGBD) images of a rectangular fabric sample, estimate pick points and pull vectors to spread the fabric to maximize coverage. To generate data, we develop a fabric simulator and an algorithmic supervisor that has access to complete state information. We train policies in simulation using domain randomization and dataset aggregation (DAgger) on three tiers of difficulty in the initial randomized configuration. We present results comparing five baseline policies to learned policies and report systematic comparisons of RGB vs D vs RGBD images as inputs. In simulation, learned policies achieve comparable or superior performance to analytic baselines. In 180 physical experiments with the da Vinci Research Kit (dVRK) surgical robot, RGBD policies trained in simulation attain coverage of 83% to 95% depending on difficulty tier, suggesting that effective fabric smoothing policies can be learned from an algorithmic supervisor and that depth sensing is a valuable addition to color alone. Supplementary material is available at https://sites.google.com/view/fabric-smoothing.

The use of flexible endoscope has been rising inconveniences. Steering of the distal section is not intuitive and the weight of the endoscope burdens a physical pressure on physicians who use it continuously for a long time. Also, the limited dexterity of an instrument makes therapeutic procedures more difficult, and further the unintended communications often occur during cooperation with assistants. These degrade the efficiency and thus increase the procedure time. In this paper, we propose a robotic endoscopy system (easyEndo) that can be mounted on a conventional endoscope and facilitate solo-endoscopy with two intuitive hand-held controllers. Furthermore, a robotic arm is presented that can be attached to the endoscope to assist with tissue traction. To validate the robotic endoscopy system, experiments to simulate biopsy and lesion marking were conducted with novices. The results showed that the robotic manipulations improved efficiency and reduced workload than manual manipulation. Subsequently, a prototype of the robotic arm was attached at the distal end of the endoscope, and the feasibility of tissue traction was confirmed by a simulation of pulling a rubber band.

Endoscopes are widely used for not only intraluminal diagnosis but also therapeutic procedures in the gastrointestinal area. However, conventional endoscopes present a few challenges such as nonintuitive manipulation, physical burden on the operator, and lack of dexterity. These challenges limit endoscope usage in complex surgical procedures. Moreover, endoscope operators undergo extensive and lengthy training to attain an adequate skill level. In this paper, we introduce a flexible surgery robot platform K-FLEX that facilitates teleoperation via an intuitive master interface and bimanual manipulation by means of two dexterous surgical robot arms. Its effects on endoscopic procedures, especially in terms of task performance, learning properties, and physical burden on the operator, are validated by conducting a user test. The experimental results demonstrate that the developed robotic assistant increases operation speed, especially for novices; simplifies the learning process; and reduces the workload on the operator compared to conventional endoscopes.

In robotics, robotic arms should be smaller, less expensive, and able to demonstrate a better performance, in order to be utilized in various fields. However, expensive actuators and speed reducers are required to generate sufficient force at the end effector after overcoming the gravitational torque of robotic arm. Robotic arms become compact and cost effective if the gravitational torque can be passively compensated. Especially in the field of robotic laparoscopic surgery, the static equilibrium state of the robot arm in the entire workspace is essential to achieve both safety and ease of use. In this study, we propose a novel passive gravity compensation mechanism based on springs and wires. This mechanism utilizes a scotch yoke mechanism to compensate the gravitational torque changed by two rotational degrees of freedom (DOF). Furthermore, we applied to a surgical robotic arm which was constructed using a roll-pitch joint. According to the experiments, it was proved that the new mechanism effectively compensate the gravitational torque. As a result, compact and cost-effective robotic arms can be developed satisfying safety and ease of use.