Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Abstract

Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialities. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to re-purpose decommissioned da Vinci surgical systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this paper, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain this initiative and build upon it.

References in corpus (14)

• Gesture Recognition in Robotic Surgery: a Review
• Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks
• Autonomous task planning and situation awareness in robotic surgery
• Improving rigid 3D calibration for robotic surgery
• Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation
• VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
• Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery
• A Reliable Gravity Compensation Control Strategy for dVRK Robotic Arms With Nonlinear Disturbance Forces
• A surgical dataset from the da Vinci Research Kit for task automation and recognition
• Learning Deep Nets for Gravitational Dynamics with Unknown Disturbance through Physical Knowledge Distillation: Initial Feasibility Study
• A Versatile Data-Driven Framework for Model-Independent Control of Continuum Manipulators Interacting With Obstructed Environments With Unknown Geometry and Stiffness
• A data-set of piercing needle through deformable objects for Deep Learning from Demonstrations
• Model-Predictive Control of Blood Suction for Surgical Hemostasis using Differentiable Fluid Simulations
• From the DESK (Dexterous Surgical Skill) to the Battlefield -- A Robotics Exploratory Study

Cited by in corpus (5)

• Automating Surgical Peg Transfer: Calibration with Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans
• Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty
• Caveats on the first-generation da Vinci Research Kit: latent technical constraints and essential calibrations
• Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos
• FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning