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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. 

Robot-assisted minimally invasive surgery has made a substantial impact in operating rooms over the past few decades with their high dexterity, small tool size, and impact on adoption of minimally invasive techniques. In recent years, intelligence and different levels of surgical robot autonomy have emerged thanks to the medical robotics endeavors at numerous academic institutions and leading surgical robot companies. To accelerate interaction within the research community and prevent repeated development, we propose the Collaborative Robotics Toolkit (CRTK), a common API for the RAVEN-II and da Vinci Research Kit (dVRK) - two open surgical robot platforms installed at more than 40 institutions worldwide. CRTK has broadened to include other robots and devices, including simulated robotic systems and industrial robots. This common API is a community software infrastructure for research and education in cutting edge human-robot collaborative areas such as semi-autonomous teleoperation and medical robotics. This paper presents the concepts, design details and the integration of CRTK with physical robot systems and simulation platforms. 

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 ℝ 3 . Additional trajectories from varying initial positions were produced from a single policy to simulate repeated passes of the hand-off task. 

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. 

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. 

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. 

Open-sourced kinematic models of the da Vinci Surgical System have previously been developed using serial chains for forward and inverse kinematics. However, these models do not describe the motion of every link in the closed-loop mechanism of the da Vinci manipulators; knowing the kinematics of all components in motion is essential for the foundation of modeling the system dynamics and implementing representative simulations. This paper proposes a modeling method of the closed-loop kinematics, using the existing da Vinci kinematics and an optical motion capture link length calibration. Resulting link lengths and DH parameters are presented and used as the basis for ROS-based simulation models. The models were simulated in RViz visualization simulation and Gazebo dynamics simulation. Additionally, the closed-loop kinematic chain was verified by comparing the remote center of motion location of simulation with the hardware. Furthermore, the dynamic simulation resulted in satisfactory joint stability and performance. All models and simulations are provided as an open-source package.