Vinci Research Kit Research Articles

Unconstrained lightweight control interface for robot-assisted minimally invasive surgery using MediaPipe framework and head-mounted display

Robotic surgery is preferred over open or laparoscopic surgeries due to its intuitiveness and convenience. However, prolonged use of surgical robots can cause neck pain and joint fatigue in wrist and fingers. Also, input systems are bulky and difficult to maintain. To resolve these issues, we propose a novel input module based on real-time 3D hand tracking driven by RGB images and MediaPipe framework to control surgical robots such as patient side manipulator (PSM) and endoscopic camera manipulator (ECM) of da Vinci research kit. In this paper, we explore the mathematical basis of the proposed 3D hand tracking module and provide a proof-of-concept through user experience (UX) studies conducted in a virtual environment. End-to-end latencies for controlling PSM and ECM were 170 ± 10 ms and 270 ± 10 ms, respectively. Of fifteen novice participants recruited for the UX study, thirteen managed to reach a qualifiable level of proficiency after 50 min of practice and fatigue of hand and wrist were imperceivable. Therefore, we concluded that we have successfully developed a robust 3D hand tracking module for surgical robot control and in the future, it would hopefully reduce hardware cost and volume as well as resolve ergonomic problems. Furthermore, RGB image driven 3D hand tracking module developed in our study can be widely applicable to diverse fields such as extended reality (XR) development and remote robot control. In addition, we provide a new standard for evaluating novel input modalities of XR environments from a UX perspective.

Cognitive load in tele-robotic surgery: a comparison of eye tracker designs.

Eye gaze tracking and pupillometry are evolving areas within the field of tele-robotic surgery, particularly in the context of estimating cognitive load (CL). However, this is a recent field, and current solutions for gaze and pupil tracking in robotic surgery require assessment. Considering the necessity of stable pupillometry signals for reliable cognitive load estimation, we compare the accuracy of three eye trackers, including head and console-mounted designs. We conducted a user study with the da Vinci Research Kit (dVRK), to compare the three designs. We collected eye tracking and dVRK video data while participants observed nine markers distributed over the dVRK screen. We compute and analyze pupil detection stability and gaze prediction accuracy for the three designs. Head-worn devices present better stability and accuracy of gaze prediction and pupil detection compared to console-mounted systems. Tracking stability along the field of view varies between trackers, with gaze predictions detected at invalid zones of the image with high confidence. While head-worn solutions show benefits in confidence and stability, our results demonstrate the need to improve eye tacker performance regarding pupil detection, stability, and gaze accuracy in tele-robotic scenarios.

Instrument Detection and Descriptive Gesture Segmentation on a Robotic Surgical Maneuvers Dataset

Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend the ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low-level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tools and the left-handed tools. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. On the other hand, for gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP.

Enhancing robotic telesurgery with sensorless haptic feedback.

This paper evaluates user performance in telesurgical tasks with the da Vinci Research Kit (dVRK), comparing unilateral teleoperation, bilateral teleoperation with force sensors and sensorless force estimation. A four-channel teleoperation system with disturbance observers and sensorless force estimation with learning-based dynamic compensation was developed. Palpation experiments were conducted with 12 users who tried to locate tumors hidden in tissue phantoms with their fingers or through handheld or teleoperated laparoscopic instruments with visual, force sensor, or sensorless force estimation feedback. In a peg transfer experiment with 10 users, the contribution of sensorless haptic feedback with/without learning-based dynamic compensation was assessed using NASA TLX surveys, measured free motion speeds and forces, environment interaction forces as well as experiment completion times. The first study showed a 30% increase in accuracy in detecting tumors with sensorless haptic feedback over visual feedback with only a 5-10% drop in accuracy when compared with sensor feedback or direct instrument contact. The second study showed that sensorless feedback can help reduce interaction forces due to incidental contacts by about 3 times compared with unilateral teleoperation. The cost is an increase in free motion forces and physical effort. We show that it is possible to improve this with dynamic compensation. We demonstrate the benefits of sensorless haptic feedback in teleoperated surgery systems, especially with dynamic compensation, and that it can improve surgical performance without hardware modifications.

Cognitive effort detection for tele-robotic surgery via personalized pupil response modeling.

Gaze tracking and pupillometry are established proxies for cognitive load, giving insights into a user's mental effort. In tele-robotic surgery, knowing a user's cognitive load can inspire novel human-machine interaction designs, fostering contextual surgical assistance systems and personalized training programs. While pupillometry-based methods for estimating cognitive effort have been proposed, their application in surgery is limited by the pupil's sensitivity to brightness changes, which can mask pupil's response to cognitive load. Thus, methods considering pupil and brightness conditions are essential for detecting cognitive effort in unconstrained scenarios. To contend with this challenge, we introduce a personalized pupil response model integrating pupil and brightness-based features. Discrepancies between predicted and measured pupil diameter indicate dilations due to non-brightness-related sources, i.e., cognitive effort. Combined with gaze entropy, it can detect cognitive load using a random forest classifier. To test our model, we perform a user study with the da Vinci Research Kit, where 17 users perform pick-and-place tasks in addition to auditory tasks known to generate cognitive effort responses. We compare our method to two baselines (BCPD and CPD), demonstrating favorable performance in varying brightness conditions. Our method achieves an average true positive rate of 0.78, outperforming the baselines (0.57 and 0.64). We present a personalized brightness-aware model for cognitive effort detection able to operate under unconstrained brightness conditions, comparing favorably to competing approaches, contributing to the advancement of cognitive effort detection in tele-robotic surgery. Future work will consider alternative learning strategies, handling the difficult positive-unlabeled scenario in user studies, where only some positive and no negative events are reliably known.

Mixed reality based teleoperation and visualization of surgical robotics.

Surgical robotics has revolutionized the field of surgery, facilitating complex procedures in operating rooms. However, the current teleoperation systems often rely on bulky consoles, which limit the mobility of surgeons. This restriction reduces surgeons' awareness of the patient during procedures and narrows the range of implementation scenarios. To address these challenges, an alternative solution is proposed: a mixed reality-based teleoperation system. This system leverages hand gestures, head motion tracking, and speech commands to enable the teleoperation of surgical robots. The implementation focuses on the da Vinci research kit (dVRK) and utilizes the capabilities of Microsoft HoloLens 2. The system's effectiveness is evaluated through camera navigation tasks and peg transfer tasks. The results indicate that, in comparison to manipulator-based teleoperation, the system demonstrates comparable viability in endoscope teleoperation. However, it falls short in instrument teleoperation, highlighting the need for further improvements in hand gesture recognition and video displayquality.

Augmenting Robot-Assisted Pattern Cutting with Periodic Perturbations-Can We Make Dry Lab Training More Realistic?

Teleoperated robot-assisted minimally-invasive surgery (RAMIS) offers many advantages over open surgery, but RAMIS training still requires optimization. Existing motor learning theories could improve RAMIS training. However, there is a gap between current knowledge based on simple movements and training approaches required for the more complicated work of RAMIS surgeons. Here, we studied how surgeons cope with time-dependent perturbations. We used the da Vinci Research Kit and investigated the effect of time-dependent force and motion perturbations on learning a circular pattern-cutting surgical task. Fifty-four participants were assigned to two experiments, with two groups for each: a control group trained without perturbations and an experimental group trained with 1Hz perturbations. In the first experiment, force perturbations alternatingly pushed participants' hands inwards and outwards in the radial direction. In the second experiment, the perturbation constituted a periodic up-and-down motion of the task platform. Participants trained with perturbations learned how to overcome them and improve their performances during training without impairing them after the perturbations were removed. Moreover, training with motion perturbations provided participants with an advantage when encountering the same or other perturbations after training, compared to training without perturbations. Periodic perturbations can enhance RAMIS training without impeding the learning of the perturbed task. Our results demonstrate that using challenging training tasks that include perturbations can better prepare surgical trainees for the dynamic environment they will face with patients in the operating room.

Caveats on the First-Generation da Vinci Research Kit: Latent Technical Constraints and Essential Calibrations

Caveats on the First-Generation da Vinci Research Kit: Latent Technical Constraints and Essential Calibrations

Robot Learning Incorporating Human Interventions in the Real World for Autonomous Surgical Endoscopic Camera Control

Recent studies in surgical robotics have focused on automating common surgical subtasks such as grasping and manipulation using deep reinforcement learning (DRL). In this work, we consider surgical endoscopic camera control for object tracking e.g. using the endoscopic camera manipulator (ECM) from the da Vinci Research Kit (dVRK) (Intuitive Inc., Sunnyvale, CA, USA) as a typical surgical robot learning task. A DRL policy for controlling the robot joint space movements is first trained in a simulation environment and then continues the learning in the real world. To speed up training and avoid significant failures (in this case, losing view of the object), human interventions are incorporated into the training process and regular DRL is combined with generative adversarial imitation learning (GAIL) to encourage imitating human behaviors. Experiments show that an average reward of 159.8 can be achieved within 1000 steps compared to only 121.8 without human interventions, and the view of the moving object is lost only twice during the training process out of 3 trials. These results show that human interventions can improve learning speed and significantly reduce failures during the training process.

Pupillometry in telerobotic surgery: A comparative evaluation of algorithms for cognitive effort estimation

Background: Eye gaze tracking and pupillometry are emerging topics in telerobotic surgery as it is believed that they will enable novel gaze-based interaction paradigms and provide insights into the user’s cognitive load (CL). Further, the successful integration of CL estimation into telerobotic systems is thought to catalyze the development of new human-computer interfaces for personalized assistance and training processes. However, this field is in its infancy, and identifying reliable gaze and pupil-tracking solutions in robotic surgery is still an area of ongoing research and high uncertainty. Methods: Considering the potential benefits of pupillometry-based CL assessments in telerobotic surgery, we seek to better understand the possibilities and limitations of contemporary pupillometry-based cognitive effort estimation algorithms in telerobotic surgery. To this end, we conducted a user study using the da Vinci Research Kit (dVRK) and performed two experiments where participants were asked to perform a series of N-Back tests, either while (i) idling or (ii) performing a peg transfer task. We then compare four contemporary CL estimation methods based on direct analysis of pupil diameter in the spatial and frequency domains. Results: We find that some methods can detect the presence of cognitive effort in simple scenarios (e.g., when the user is not performing any manual task), they fail to differentiate the different levels of CL. Similarly, when the manual peg transfer task is added, the reliability of all models is compromised, highlighting the necessity of more robust methods that consider different factors that complement the pupil diameter information. Conclusion: Our results offer a quantitative perspective of the limitations of the current solutions and highlight the necessity of developing tailored designs for the telerobotic surgery environment.

Towards Human-Robot Collaborative Surgery: Trajectory and Strategy Learning in Bimanual Peg Transfer

While the traditional control of surgical robots relies on fully manual teleoperations, human-robot collaborative systems promise to address issues such as workspace constrains and laborious tasks. In particular, shared control can reduce the surgeon's workload and improve the overall surgical performance by supporting the surgeon effort during movements while keeping them in charge of complex control phases. In this letter, we propose a segmentation of the bimanual peg transfer task that alternates manual and autonomous control correspondingly. The authority allocation in this shared control framework considers both the limitation of learning-based methods and the higher dexterity of humans during physical interaction. The motion and strategies are transferred from an expert human to a da Vinci Research Kit (dVRK) [1] using an epsilon-greedy on a maximum entropy inverse reinforcement learning algorithm. The model generated enables to train an intelligent agent that can skillfully collaborate with the human operator during the surgical task. The proposed shared control framework is verified both on a virtual platform and then on a real dVRK to assess its usability and robustness. The results show that, compared to traditional teleoperation, our method can achieve faster and more consistent peg transfers. An analysis of the participants' effort also reveals a significantly lower perception of the workload.

Robotic Manipulation of Deformable Rope-Like Objects Using Differentiable Compliant Position-Based Dynamics

Robot manipulation of rope-like objects is an interesting problem with some critical applications, such as autonomous robotic suturing. Solving for and controlling rope is difficult due to the complexity of rope physics and the challenge of building fast and accurate models of deformable materials. While more data-driven approaches have become more popular for finding controllers that learn to do a single task, there is still a strong motivation for a model-based method that could solve many optimization problems. Towards this end, we introduced compliant position-based dynamics (XPBD) to model rope-like objects. Using geometric constraints, the model can represent the coupling of shear/stretch and bend/twist effects. Of crucial importance is that our formulation is differentiable, which can solve parameter estimation problems and improve the matching of rope physics to real-life scenarios (i.e., the real-to-sim problem). For the generality of rope-like objects, two different solvers are proposed to handle the inextensible and extensible effects of varied material stiffness for the rope. We demonstrate our framework's robustness and accuracy on real-to-sim experimental setups using the Baxter robot and the da Vinci research kit (DVRK) [1]. Our work leads to a new path for robotic manipulation of the deformable rope-like object taking advantage of the ready-to-use gradients.

FRSR: Framework for real-time scene reconstruction in robot-assisted minimally invasive surgery

3D reconstruction of the intra-operative scenes provides precise position information which is the foundation of various safety related applications in robot-assisted surgery, such as augmented reality. Herein, a framework integrated into a known surgical system is proposed to enhance the safety of robotic surgery. In this paper, we present a scene reconstruction framework to restore the 3D information of the surgical site in real time. In particular, a lightweight encoder–decoder network is designed to perform disparity estimation, which is the key component of the scene reconstruction framework. The stereo endoscope of da Vinci Research Kit (dVRK) is adopted to explore the feasibility of the proposed approach, and it provides the possibility for the migration to other Robot Operating System (ROS) based robot platforms due to the strong independence on hardware. The framework is evaluated using three different scenarios, including a public dataset (3018 pairs of endoscopic images), the scene from the dVRK endoscope in our lab as well as a self-made clinical dataset captured from an oncology hospital. Experimental results show that the proposed framework can reconstruct 3D surgical scenes in real time (25 FPS), and achieve high accuracy (2.69 ± 1.48 mm in MAE, 5.47 ± 1.34 mm in RMSE and 0.41 ± 0.23 in SRE, respectively). It demonstrates that our framework can reconstruct intra-operative scenes with high reliability of both accuracy and speed, and the validation of clinical data also shows its potential in surgery. This work enhances the state of art in 3D intra-operative scene reconstruction based on medical robot platforms. The clinical dataset has been released to promote the development of scene reconstruction in the medical image community.

Integrated planning and control of robotic surgical instruments for task autonomy

Agile maneuvers are essential for robot-enabled complex tasks such as surgical procedures. Prior explorations on surgery autonomy are limited to feasibility study of completing a single task without systematically addressing generic manipulation safety across different tasks. We present an integrated planning and control framework for 6-DoF robotic instruments for pipeline automation of surgical tasks. We leverage the geometry of a robotic instrument and propose the nodal state space to represent the robot state in SE (3) space. Each elementary robot motion could be encoded by regulation of the state parameters via a dynamical system. This theoretically ensures that every in-process trajectory is globally feasible and stably reached to an admissible target, and the controller is of closed-form without computing 6-DoF inverse kinematics. Then, to plan the motion steps reliably, we propose an interactive (instant) goal state of the robot that transforms manipulation planning through desired path constraints into a goal-varying manipulation (GVM) problem. We detail how GVM could adaptively and smoothly plan the procedure (could proceed or rewind the process as needed) based on on-the-fly situations under dynamic or disturbed environment. Finally, we extend the above policy to characterize complete pipelines of various surgical tasks. Simulations show that our framework could smoothly solve twisted maneuvers while avoiding collisions. Physical experiments using the da Vinci Research Kit validates the capability of automating individual tasks including tissue debridement, dissection, and wound suturing. The results confirm good task-level consistency and reliability compared to state-of-the-art automation algorithms.

Uncertainty-Aware Self-Supervised Learning for Cross-Domain Technical Skill Assessment in Robot-Assisted Surgery

Objective technical skill assessment is crucial for effective training of new surgeons in robot-assisted surgery. With advancements in surgical training programs in both physical and virtual environments, it is imperative to develop generalizable methods for automatically assessing skills. In this paper, we propose a novel approach for skill assessment by transferring domain knowledge from labeled kinematic data to unlabeled data. Our approach leverages labeled data from common surgical training tasks such as Suturing, Needle Passing, and Knot Tying to jointly train a model with both labeled and unlabeled data. Pseudo labels are generated for the unlabeled data through an iterative manner that incorporates uncertainty estimation to ensure accurate labeling. We evaluate our method on a virtual reality simulated training task (Ring Transfer) using data from the da Vinci Research Kit (dVRK). The results show that trainees with robotic assistance have significantly higher expert probability compared to these without any assistance, p<0.05, which aligns with previous studies showing the benefits of robotic assistance in improving training proficiency. Our method offers a significant advantage over other existing works as it does not require manual labeling or prior knowledge of the surgical training task for robot-assisted surgery.

A Step Towards Conditional Autonomy - Robotic Appendectomy

In recent years, Robot-Assisted Minimally Invasive Surgery (RAMIS) has been widely adopted worldwide due to its high precision, improved ergonomics and intuitive control. With the advances in artificial intelligence and surgical robot technologies, it is anticipated that the cognitive load on the surgeons could be relieved with a higher level of autonomy on surgical robots as well as simultaneously improving the precision of surgical robots' operations, minimising the risk of human errors and leading to safer operations. However, much research is still focused on task autonomy due to the complexity of the operations, and the limited clinical data on robotic surgery in some specific scenarios. Therefore, recent research has widely used simulated surgery scenarios to collect data to train artificial intelligence (AI) models. In this paper, we propose a method as a step towards conditional autonomy for robotic appendectomy. To achieve conditional autonomy, demonstration data was collected from human operators performing the appendectomy in a simulated robotic scene. The robotic instrument movements and trajectories for performing the appendectomy were learned from the demonstrated data. Once the surgeon specifies the location of the appendix, the proposed framework can carry out the appendectomy autonomously by using the dynamic motion primitives (DMP) generated from the learned surgical movements. Through the extensive validations in a simulated environment and on the da Vinci Research Kit (dVRK), we have shown that the proposed method can carry out the appendectomy procedure semi-automatically. Based on five metrics, it shows that the framework can reduce the total task path length, completion time and appendix stump length while maintaining a high degree of similarity to the demonstrated trajectory. This work demonstrated the feasibility of conditional autonomy in robotic surgeries.

Automating Surgical Peg Transfer: Calibration With Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans

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 at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/surgicalpegtransfer</uri> Note 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.

A Surgical Robotic Framework for Safe and Autonomous Data-Driven Learning and Manipulation of an Unknown Deformable Tissue with an Integrated Critical Space

Aside from reliable robotic hardware and sensing technologies, to successfully transition from teleoperation to an autonomous and safe minimally invasive robotic surgery on unknown Deformable Tissues (U-DTs), various challenges need to be simultaneously considered and tackled to ensure safety and accuracy of the procedure. These challenges mainly include but are not limited to online modeling and reliable tracking of a U-DT with integrated critical tissues as well as development of reliable and fast control algorithms to enable safe, accurate, and autonomous surgical procedures. To collectively and simultaneously address these challenges and toward performing an autonomous and safe minimally invasive robotic surgery in a confined environment, in this paper, we present a surgical robotic framework with (i) real-time vision-based detection algorithm based on a Convolutional Neural Network (CNN) architecture that enables tracking the time-varying deformation of a critical tissue located within a U-DT and (ii) a complementary data-driven adaptive constrained optimization approach that learns deformation behavior of a U-DT while autonomously manipulating it within a time-varying constrained environment defined based on the output of the CNN detection algorithm. To thoroughly evaluate the performance of the proposed framework, we used the da Vinci Research Kit (dVRK) and performed various experiments on a custom-designed U-DT phantom with an arbitrary deformable vessel embedded within the phantom’s body (serving as the U-DT’s integrated critical space). Various experiments were conducted and analyzed to demonstrate the performance of the proposed framework and ensure robustness and safety while performing an autonomous surgical procedure.

A CoppeliaSim Dynamic Simulator for the Da Vinci Research Kit

The design of a physics-based dynamic simulator of a robot requires to properly integrate the robot kinematic and dynamic properties in a virtual environment. Naturally, the closer is the integrated information to the real robot properties, the more accurate the simulator predicts the real robot behaviour. A reliable robot simulator is a valuable asset for developing new research ideas; its use dramatically reduces the costs and it is available to all researchers. This letter presents a dynamic simulator of the da Vinci Research Kit (dVRK) patient-side manipulator (PSM). The kinematic and dynamic properties of the simulator rely on the parameters identified in Wang et al. With respect to the kinematic simulator previously developed by some of the authors, this work: (i) redefines the kinematic architecture and the actuation model by modeling the double parallelogram and the counterweight mechanism, to reflect the structure of the real robot; (ii) integrates the identified dynamic parameters in the simulation model. The obtained simulator enables the design and validation of control strategies relying on the robot dynamic model, including interaction force estimation and control, that are fundamental to guarantee safety in many surgical tasks.

A Unified Monocular Camera-Based and Pattern-Free Hand-to-Eye Calibration Algorithm for Surgical Robots With RCM Constraints

With progressive advancements in modern technologies, image-guided automation, which plans and manipulates particular tasks by utilizing visual cues, is becoming an emerging sector in medical robotics. Its prevalence nowadays requires a reliable hand-to-eye calibration owing to various configurations of surgical robots, especially for those with remote center of motion (RCM) constraints, e.g., da Vinci Research Kit (dVRK). In this article, we proposed a novel unified calibration method, which leverages the structure characteristics and establishes an explicit geometric model to solve the hand-to-eye relationship only with a monocular camera’s feedback. By freely moving the patient side manipulator in dVRK, our method can first recover the 3-D poses of its shaft at various configurations using Plücker coordinates. To obtain the hand-to-eye translation relationship, these recovered poses are further converged by softly intersecting them within a minimal spatial sphere while preserving their coordinates using an aggregating sphere loss. Meanwhile, the orientation mapping can be recovered by leveraging the corresponding longitudinal-axis information. To improve the calibration accuracy, a local optimization strategy that utilizes the bundle adjustment to refine the transformation using the previous outcomes is also introduced. With such a hierarchical calibration scheme, the hand-to-eye relationship of any RCM constrained robot can be successfully achieved. Extensive simulations and experiments are conducted based on dVRK platform. By comparing with the traditional method, the validation results prove the feasibility and superiority of our unified algorithm in calibration accuracy and robustness.