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Remarkable progress has been made in the field of robot-assisted surgery in recent years, particularly in the area of surgical task automation, though many challenges and opportunities still exist. Among these topics, the detection and tracking of surgical tools play a pivotal role in enabling autonomous systems to plan and execute procedures effectively. For instance, accurate estimation of a needle’s position and posture is essential for surgical systems to grasp the needle and perform suturing tasks autonomously. In this paper, we developed image-based methods for markerless 6 degrees of freedom (DOF) suture needle pose estimation using keypoint detection technique based on Deep Learning and Point-to-point Registration, we also leveraged multi-viewpoint from a robotic endoscope to enhance the accuracy. The data collection and annotation process was automated by utilizing a simulated environment, enabling us to create a dataset with 3446 evenly distributed needle samples across a suturing phantom space for training and to demonstrate more convincing and unbiased performance results. We also investigated the impact of training set size on the keypoint detection accuracy. Our implemented pipeline that takes a single RGB image achieved a median position error of 1.4 mm and a median orientation error of 2.9^∘, while our multi-viewpoint method was able to further reduce the random errors. 

Advancements in robot-assisted surgery have been rapidly growing since two decades ago. More recently, the automation of robotic surgical tasks has become the focus of research. In this area, the detection and tracking of a surgical tool are crucial for an autonomous system to plan and perform a procedure. For example, knowing the position and posture of a needle is a prerequisite for an automatic suturing system to grasp it and perform suturing tasks. In this paper, we proposed a novel method, based on Deep Learning and Point-to-point Registration, to track the 6 degrees of freedom (DOF) pose of a metal suture needle from a robotic endoscope (an Endoscopic Camera Manipulator from the da Vinci Robotic Surgical Systems), without the help of any marker. The proposed approach was implemented and evaluated in a standard simulated surgical environment provided by the 2021–2022 AccelNet Surgical Robotics Challenge, thus demonstrates the potential to be translated into a real-world scenario. A customized dataset containing 836 images collected from the simulated scene with ground truth of poses and key points information was constructed to train the neural network model. The best pipeline achieved an average position error of 1.76 mm while the average orientation error is 8.55 degrees, and it can run up to 10 Hz on a PC. 

There is a large community of people with hand disabilities, and these disabilities can be a barrier to those looking to retain or pursue surgical careers. With the development of surgical robotics technologies, it may be possible to develop user interfaces to accommodate these individuals. This paper proposes a hand-free control method for the gripper of a patient side manipulator (PSM) in the da Vinci surgical system. Using electromyography (EMG) signals, a proportional control method was tested on its ability to grasp a pressure sensor. These preliminary results demonstrate that the user can reliably control the grasping motion of the da Vinci PSM using this system. There is a strong correlation between grasping force and normalized EMG signal (r= 0.874). Moreover, the gripper can generate a step grasping force output when feeding in a generated step signal. The results in this paper demonstrate the system integration of a research EMG system with the da Vinci surgical system and are a step towards developing accessible teleoperation systems for surgeons with disabilities. Hand-free control for remaining degrees of freedom in the PSM is under development using additional input from the motion capture system. 

Recently, Reinforcement Learning (RL) techniques have seen significant progress in the robotics domain. This can be attributed to robust simulation frameworks that offer realistic environments to train. However, there is a lack of platforms which offer environments that are conducive to medical robotic tasks. Having earlier designed the Asynchronous Multibody Framework (AMBF) - a real-time dynamics simulator well-suited for medical robotics tasks, we propose an open source AMBF-RL (ARL) toolkit to assist in designing control algorithms for these robots, as well as a module to collect and parse expert demonstration data. We validate ARL by attempting to partially automate the task of debris removal on the da Vinci Research Kit (dVRK) Patient Side Manipulator (PSM) in simulation by calculating the optimal policy using both Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) with DDPG. The trained policies are successfully transferred onto the physical dVRK PSM and tested. Finally, we draw a conclusion from the results and discuss our observations of the experiments conducted. 

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. 

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.