3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (e.g. HyperCD [1]), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses (weighted CD) that requires no parameter tuning. To this end, we propose a search scheme, Loss Distillation via Gradient Matching, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely Landau CD, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. Our demo code is available at https://github.com/Zhang-VISLab/IROS2024-LossDistillationWeightedCD.

Haptic feedback to the surgeon during robotic surgery would enable safer and more immersive surgeries but estimating tissue interaction forces at the tips of robotically controlled surgical instruments has proven challenging. Few existing surgical robots can measure interaction forces directly and the additional sensor may limit the life of instruments. We present a hybrid model and learning-based framework for force estimation for the Patient Side Manipulators (PSM) of a da Vinci Research Kit (dVRK). The model-based component identifies the dynamic parameters of the robot and estimates free-space joint torque, while the learning-based component compensates for environmental factors, such as the additional torque caused by trocar interaction between the PSM instrument and the patient’s body wall. We evaluate our method in an abdominal phantom and achieve an error in force estimation of under 10% normalized root-mean-squared error. We show that by using a model-based method to perform dynamics identification, we reduce reliance on the training data covering the entire workspace. Although originally developed for the dVRK, the proposed method is a generalizable framework for other compliant surgical robots. The code is available at https://github.com/vu-maple-lab/dvrk_force_estimation.

The success rate of robotic-assisted needle-guided interventions for applications such as tissue biopsy and targeted drug delivery relies heavily on the accuracy of the needle placement. Tissue shift and needle tip deflection due to needle-tissue interaction are some factors that can adversely affect the outcome of these procedures. In this paper, we present a novel algorithm for robotically-steered bevel tip needles that provides variable needle curvatures by continuously controlling the rotation speed of the needle in a bidirectional manner. Our algorithm is an extension of the Continuous Rotation and Variable Curvature (CURV) algorithm and extends its use with wired sensorized needles. Additionally, we present algorithms for the implementation of our proposed method for closed-loop needle steering in robotic systems with image or sensor feedback. To validate our approach, we perform two benchtop needle insertion experiments in a gelatin phantom and ex vivo tissue. In the first experiment, we demonstrate the capability of our proposed algorithm in achieving variable curvatures and compare it with the CURV algorithm and our simulation results. The second experiment studies the effect of the unidirectional and bidirectional needle steering on the tissue wind-up using a novel force collection setup. Our results highlight the capability of the proposed algorithm in achieving variable curvature profiles and suggest a potential advantage compared to the original method in terms of reducing the imbalanced forces sensed at the load cell due to the needle-tissue friction buildup.

Human-robot shared control, which integrates the advantages of both humans and robots, is an effective approach to facilitate efficient surgical operation. Learning from demonstration (LfD) techniques can be used to automate some of the surgical sub tasks for the construction of the shared control mechanism. However, a sufficient amount of data is required for the robot to learn the manoeuvres. Using a surgical simulator to collect data is a less resource-demanding approach. With sim-to-real adaptation, the manoeuvres learned from a simulator can be transferred to a physical robot. To this end, we propose a sim-to-real adaptation method to construct a human-robot shared control framework for robotic surgery. In this paper, a desired trajectory is generated from a simulator using LfD method, while dynamic motion primitives (DMP) is used to transfer the desired trajectory from the simulator to the physical robotic platform. Moreover, a role adaptation mechanism is developed such that the robot can adjust its role according to the surgical operation contexts predicted by a neural network model. The effectiveness of the proposed framework is validated on the da Vinci Research Kit (dVRK). Results of the user studies indicated that with the adaptive human-robot shared control framework, the path length of the remote controller, the total clutching number and the task completion time can be reduced significantly. The proposed method outperformed the traditional manual control via teleoperation.

Magnetic resonance imaging (MRI) can provide high-quality 3-D visualization of target anatomy, surrounding tissue, and instrumentation, but there are significant challenges in harnessing it for effectively guiding interventional procedures. Challenges include the strong static magnetic field, rapidly switching magnetic field gradients, high-power radio frequency pulses, sensitivity to electrical noise, and constrained space to operate within the bore of the scanner. MRI has a number of advantages over other medical imaging modalities, including no ionizing radiation, excellent soft-tissue contrast that allows for visualization of tumors and other features that are not readily visible by other modalities, true 3-D imaging capabilities, including the ability to image arbitrary scan plane geometry or perform volumetric imaging, and capability for multimodality sensing, including diffusion, dynamic contrast, blood flow, blood oxygenation, temperature, and tracking of biomarkers. The use of robotic assistants within the MRI bore, alongside the patient during imaging, enables intraoperative MR imaging (iMRI) to guide a surgical intervention in a closed-loop fashion that can include tracking of tissue deformation and target motion, localization of instrumentation, and monitoring of therapy delivery. With the ever-expanding clinical use of MRI, MRI-compatible robotic systems have been heralded as a new approach to assist interventional procedures to allow physicians to treat patients more accurately and effectively. Deploying robotic systems inside the bore synergizes the visual capability of MRI and the manipulation capability of robotic assistance, resulting in a closed-loop surgery architecture. This article details the challenges and history of robotic systems intended to operate in an MRI environment and outlines promising clinical applications and associated state-of-the-art MRI-compatible robotic systems and technology for making this possible.

We present an open-source framework that provides a low barrier to entry for real-time simulation, visualization, and interactive manipulation of user-specifiable soft-bodies, environments, and robots (using a human-readable front-end interface). The simulated soft-bodies can be interacted by a variety of input interface devices including commercially available haptic devices, game controllers, and the Master Tele-Manipulators (MTMs) of the da Vinci Research Kit (dVRK) with real-time haptic feedback. We propose this framework for carrying out multi-user training, user-studies, and improving the control strategies for manipulation problems. In this paper, we present the associated challenges to the development of such a framework and our proposed solutions. We also demonstrate the performance of this framework with examples of soft-body manipulation and interaction with various input devices.

Interactive simulators are used in several important applications which include the training simulators for teleoperated robotic laparoscopic surgery. While stateof-art simulators are capable of rendering realistic visuals and accurate dynamics, grasping is often implemented using kinematic simplification techniques that prevent truly multimanual manipulation, which is often an important requirement of the actual task. Realistic grasping and manipulation in simulation is a challenging problem due to the constraints imposed by the implementation of rigid-body dynamics and collision computation techniques in state-of-the-art physics libraries. We present a penalty based parametric approach to achieve multi-manual grasping and manipulation of complex objects at arbitrary postures in a real-time dynamic simulation. This approach is demonstrated by accomplishing multi-manual tasks modeled after realistic scenarios, which include the grasping and manipulation of a two-handed screwdriver task and the manipulation of a deformable thread.

The recent development of Robot-Assisted Minimally Invasive Surgery (RAMIS) has brought much benefit to ease the performance of complex Minimally Invasive Surgery (MIS) tasks and lead to more clinical outcomes. Compared to direct master-slave manipulation, semi-autonomous control for the surgical robot can enhance the efficiency of the operation, particularly for repetitive tasks. However, operating in a highly dynamic in-vivo environment is complex. Supervisory control functions should be included to ensure flexibility and safety during the autonomous control phase. This paper presents a haptic rendering interface to enable supervised semi-autonomous control for a surgical robot. Bayesian optimization is used to tune user-specific parameters during the surgical training process. User studies were conducted on a customized simulator for validation. Detailed comparisons are made between with and without the supervised semi-autonomous control mode in terms of the number of clutching events, task completion time, master robot end-effector trajectory and average control speed of the slave robot. The effectiveness of the Bayesian optimization is also evaluated, demonstrating that the optimized parameters can significantly improve users' performance. Results indicate that the proposed control method can reduce the operator's workload and enhance operation efficiency.

Over the past decade, Robot-Assisted Surgeries (RAS), have become more prevalent in facilitating successful operations. Of the various types of RAS, the domain of collaborative surgery has gained traction in medical research. Prominent examples include providing haptic feedback to sense tissue consistency, and automating sub-tasks during surgery such as cutting or needle hand-off - pulling and reorienting the needle after insertion during suturing. By fragmenting suturing into automated and manual tasks the surgeon could essentially control the process with one hand and also circumvent workspace restrictions imposed by the control interface present at the surgeon's side during the operation. This paper presents an exploration of a discrete reinforcement learning-based approach to automate the needle hand-off task. Users were asked to perform a simple running suture using the da Vinci Research Kit. The user trajectory was learnt by generating a sparse reward function and deriving an optimal policy using Q-learning. Trajectories obtained from three learnt policies were compared to the user defined trajectory. The results showed a root-mean-square error of [0.0044mm, 0.0027mm, 0.0020mm] in ℝ3. Additional trajectories from varying initial positions were produced from a single policy to simulate repeated passes of the hand-off task.

Robot Dynamic Simulators offer convenient implementation and testing of physical robots, thus accelerating research and development. While existing simulators support most real-world robots with serially linked kinematic and dynamic chains, they offer limited or conditional support for complex closed-loop robots. On the other hand, many of the underlying physics computation libraries that these simulators employ support closed-loop kinematic chains and redundant mechanisms. Such mechanisms are often utilized in surgical robots to achieve constrained motions (e.g., the remote center of motion (RCM)). To deal with such robots, we propose a new simulation framework based on a front-end description format and a robust real-time dynamic simulator. Although this study focuses on surgical robots, the proposed format and simulator are applicable to any type of robot. In this manuscript, we describe the philosophy and implementation of the front-end description format and demonstrate its performance and the simulator’s capabilities using simulated models of real-world surgical robots.

Surgical robots for laparoscopy consist of several patient side slave manipulators that are controlled via surgeon operated master telemanipulators. Commercial surgical robots do not perform any sub-tasks - even of repetitive or noninvasive nature - autonomously or provide intelligent assistance. While this is primarily due to safety and regulatory reasons, the state of such automation intelligence also lacks the reliability and robustness for use in high-risk applications. Recent developments in continuous control using Artificial Intelligence and Reinforcement Learning have prompted growing research interest in automating mundane sub-tasks. To build on this, we present an inspired Asynchronous Framework which incorporates realtime dynamic simulation - manipulable with the masters of a surgical robot and various other input devices - and interfaces with learning agents to train and potentially allow for the execution of shared sub-tasks. The scope of this framework is generic to cater to various surgical (as well as non-surgical) training and control applications. This scope is demonstrated by examples of multi-user and multi-manual applications which allow for realistic interactions by incorporating distributed control, shared task allocation and a well-defined communication pipe-line for learning agents. These examples are discussed in conjunction with the design philosophy, specifications, system-architecture and metrics of the Asynchronous Framework and the accompanying Simulator. We show the stability of Simulator while achieving real-time dynamic simulation and interfacing with several haptic input devices and a training agent at the same time.

The use of magnetic resonance imaging (MRI) for guiding robotic surgical devices has shown great potential for performing precisely targeted and controlled interventions. To fully realize these benefits, devices must work safely within the tight confines of the MRI bore without negatively impacting image quality. Here we expand on previous work exploring MRI guided robots for neural interventions by presenting the mechanical design and assessment of a device for positioning, orienting, and inserting an interstitial ultrasound-based ablation probe. From our previous work we have added a 2 degree of freedom (DOF) needle driver for use with the aforementioned probe, revised the mechanical design to improve strength and function, and performed an evaluation of the mechanism's accuracy and effect on MR image quality. The result of this work is a 7-DOF MRI robot capable of positioning a needle tip and orienting it's axis with accuracy of 1.37 ± 0.06mm and 0.79° ± 0.41°, inserting it along it's axis with an accuracy of 0.06 ± 0.07mm, and rotating it about it's axis to an accuracy of 0.77° ± 1.31°. This was accomplished with no significant reduction in SNR caused by the robot's presence in the MRI bore, <; 10.3% reduction in SNR from running the robot's motors during a scan, and no visible paramagnetic artifacts.

The use of robotics for laparoscopic surgery has been an established field for over a decade. However, with the influx of advanced tools and algorithms for general purpose robotics, there is a need to incorporate these advancements into medical robotics technology. The daVinci Research Kit and its software framework provides a step towards these advancements. This paper presents the development of new tools and utilization of previously developed tools used for general purpose robotics, and their tailored use in medical robotics. Additionally, a method for computing haptic forces for tele-operated surgical robots is presented. The technique utilizes elastic, Spherical Proxy Regions (SPR) to readily compute directional interaction forces and manipulate them to create a dynamic behavior at the surgeon/user's manipulator.

We present a telerobotics research platform that provides complete access to all levels of control via open-source electronics and software. The electronics employs an FPGA to enable a centralized computation and distributed I/O architecture in which all control computations are implemented in a familiar development environment (Linux PC) and low-latency I/O is performed over an IEEE-1394a (FireWire) bus at speeds up to 400 Mbits/sec. The mechanical components are obtained from retired first-generation da Vinci ® Surgical Systems. This system is currently installed at 11 research institutions, with additional installations underway, thereby creating a research community around a common open-source hardware and software platform.

This paper undertakes the modular design and development of a minimally invasive surgical manipulator for MRI-guided transperineal prostate interventions. Severe constraints for the MRI-compatibility to hold the minimum artifact on the image quality and dimensions restraint of the bore scanner shadow the design procedure. Regarding the constructive design, the manipulator kinematics has been optimized and the effective analytical needle workspace is developed and followed by proposing the workflow for the manual needle insertion. A study of the finite element analysis is established and utilized to improve the mechanism weaknesses under some inevitable external forces to ensure the minimum structure deformation. The procedure for attaching a sterile plastic drape on the robot manipulator is discussed. The introduced robotic manipulator herein is aimed for the clinically prostate biopsy and brachytherapy applications.

This paper presents a surgical master-slave tele-operation system for percutaneous interventional procedures under continuous magnetic resonance imaging (MRI) guidance. This system consists of a piezoelectrically actuated slave robot for needle placement with integrated fiber optic force sensor utilizing Fabry-Perot interferometry (FPI) sensing principle. The sensor flexure is optimized and embedded to the slave robot for measuring needle insertion force. A novel, compact opto-mechanical FPI sensor interface is integrated into an MRI robot control system. By leveraging the complementary features of pneumatic and piezoelectric actuation, a pneumatically actuated haptic master robot is also developed to render force associated with needle placement interventions to the clinician. An aluminum load cell is implemented and calibrated to close the impedance control loop of the master robot. A force-position control algorithm is developed to control the hybrid actuated system. Teleoperated needle insertion is demonstrated under live MR imaging, where the slave robot resides in the scanner bore and the user manipulates the master beside the patient outside the bore. Force and position tracking results of the master-slave robot are demonstrated to validate the tracking performance of the integrated system. It has a position tracking error of 0.318mm and sine wave force tracking error of 2.227N.

This paper presents a versatile magnetic resonance imaging (MRI) compatible concentric tube continuum robotic system. The system enables MR image-guided placement of a curved, steerable active cannula. It is suitable for a variety of clinical applications including image-guided neurosurgery and percutaneous interventions, along with procedures that involve accessing a desired image target, through a curved trajectory. This 6 degree-of-freedom (DOF) robotic device is piezoelectrically actuated to provide precision motion with joint-level precision of better than 0.03mm, and is fully MRI-compatible allowing simultaneous robotic motion and imaging with no image quality degradation. The MRI compatibility of the robot has been evaluated under 3 Tesla MRI using standard prostate imaging sequences, with an average signal to noise ratio loss of less than 2% during actuator motion. The accuracy of active cannula control was evaluated in benchtop trials using an external optical tracking system with RMS error in tip placement of 1.00mm. Preliminary phantom trials of three active cannula placements in the MRI scanner showed cannula trajectories that agree with our kinematic model, with a RMS tip placement error of 0.61 - 2.24 mm.

This paper presents the first prototype of a magnetic resonance imaging (MRI) compatible piezoelectric actuated robot integrated with a high-resolution fiber optic sensor for prostate brachytherapy with real-time in situ needle steering capability in 3T MRI. The 6-degrees-of-freedom (DOF) robot consists of a modular 3-DOF needle driver with fiducial tracking frame and a 3-DOF actuated Cartesian stage. The needle driver provides needle cannula rotation and translation (2-DOF) and stylet translation (1-DOF). The driver mimics the manual physician gesture by two point grasping. To render proprioception associated with prostate interventions, a Fabry Perot interferometer based fiber optic strain sensor is designed to provide high-resolution axial needle insertion force measurement and is robust to large range of temperature variation. The paper explains the robot mechanism, controller design, optical modeling and opto-mechanical design of the force sensor. MRI compatibility of the robot is evaluated under 3T MRI using standard prostate imaging sequences and average signal noise ratio (SNR) loss is limited to 2% during actuator motion. A dynamic needle insertion is performed and bevel tip needle steering capability is demonstrated under continuous real-time MRI guidance, both with no visually identifiable interference during robot motion. Fiber optic sensor calibration validates the theoretical modeling with satisfactory sensing range and resolution for prostate intervention.

Magnetic Resonance Imaging (MRI) guided prostate biopsy and brachytherapy has been introduced in order to enhance the cancer detection and treatment. For the accurate needle positioning, a number of robotic assistants have been developed. However, problems exist due to the strong magnetic field and limited workspace. Pneumatically actuated robots have shown the minimum distraction in the environment but the confined workspace limits optimal robot design and thus controllability is often poor. To overcome the problem, a simple external damping mechanism using timing belts was sought and a 1-DOF mechanism test result indicated sufficient positioning accuracy. Based on the damping mechanism and modular system design approach, a new workspace-optimized 4-DOF parallel robot was developed for the MRI-guided prostate biopsy and brachytherapy. A preliminary evaluation of the robot was conducted using previously developed pneumatic controller and satisfying results were obtained.

Deep brain stimulation (DBS) is a technique for influencing brain function though the use of implanted electrodes. Direct magnetic resonance (MR) image guidance during DBS insertion would provide many benefits; most significantly, interventional MRI can be used for planning, monitoring of tissue deformation, real-time visualization of insertion, and confirmation of placement. The accuracy of standard stereotactic insertion is limited by registration errors and brain movement during surgery. With real-time acquisition of high-resolution MR images during insertion, probe placement can be confirmed intra-operatively. Direct MR guidance has not yet taken hold because it is often confounded by a number of issues including: MR-compatibility of existing stereotactic surgery equipment and patient access in the scanner bore. The high resolution images required for neurosurgical planning and guidance require high-field MR (1.5-3 T); thus, any system must be capable of working within the constraints of a closed, long-bore diagnostic magnet. Currently, no technological solution exists to assist MRI guided neurosurgical interventions in an accurate, simple, and economical manner.We present the design of a robotic assistant system that overcomes these difficulties and promises safe and reliable electrode placement in the brain inside closed high-field MRI scanners. The robot performs the insertion under real-time 3 T MR image guidance. This paper described analysis of the workspace requirements, MR compatibility evaluation, and mechanism design.

Magnetic Resonance Imaging (MRI) has potential to be a superior medical imaging modality for guiding and monitoring prostatic interventions. The strong magnetic field prevents the use of conventional mechatronics and the confined physical space makes it extremely challenging to access the patient. We have designed a robotic assistant system that overcomes these difficulties and promises safe and reliable intra-prostatic needle placement inside closed high-field MRI scanners. The robot performs needle insertion under real-time 3T MR image guidance; workspace requirements, MR compatibility, and workflow have been evaluated on phantoms. The paper explains the robot mechanism and controller design and presents results of preliminary evaluation of the system.

Prostate cancer biopsy is a routine medical procedure, yet conventional techniques suffer from low sensitivity attributed to suboptimal image guidance and needle placement. Targeting small lesions and foci (5 mm in diameter) is particularly prone to errors. We developed an integrated system to perform robot-assisted transperineal needle insertions into the prostate, under Magnetic Resonance Imaging (MRI) guidance. The system provides arbitrary needle trajectories and allows for simultaneous surveillance and correction of the needle path, based on intra-operative MRI. System functionality and data transfer and processing tests were conducted. Five lesions embedded in the gel phantom were targeted successfully, while communication delays (due to higher image frame rates) had no adverse affect on robot-software communication. The system was sufficiently resistant to high network loads and performed with an acceptable transfer rate.

There has been increased interest in minimally invasive ablative treatments that typically require precise placement of the ablator tool to meet the predefined planning and lead to efficient tumor destruction. Standard ablative procedures involve free hand transcutaneous ultrasonography (TCUS) in conjunction with manual tool positioning. Unfortunately, existing TCUS systems suffer from many limitations and result in failure to identify nearly half of all treatable liver lesions. Freehand manipulation of the ultrasound (US) probe and ablator tool lacks the critical level of control, accuracy, stability, and guaranteed performance required for these procedures. Freehand US results in undefined gap distribution, anatomic deformation due to variable pressure from the sonographer's hand, and severe difficulty in maintaining optimal scanning position. In response to these limitations, we propose the use of a dual robotic arm system that manages both ultrasound manipulation and needle guidance. We report a prototype of the dual arm system and a comparative performance analysis between robotic vs. freehand systems, for both US scanning and needle placement in mechanical and animal tissue phantoms.