An open-source research kit for the da Vinci® Surgical System

By Peter Kazanzides, Zihan Chen, A. Deguet, Gregory S. Fischer, Russell H. Taylor, Simon Peter DiMaio. IEEE International Conference on Robotics and Automation, 2014

564 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Mechanical Engineering, Surgery

View Paper: https://www.semanticscholar.org/paper/014da00e522ee5a5391a615d855287fb2d4a8f54

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Vladimir I. Iglovikov
2. Alexey A. Shvets
3. Chelsea Finn
4. Animesh Garg
5. Sanjay Krishnan
6. Ken Goldberg
7. Brijen Thananjeyan
8. Krishnan Srinivasan

In Mechanical Engineering

1. Suraj Nair
2. Ken Goldberg
3. Animesh Garg
4. Krishnan Srinivasan
5. Chelsea Finn
6. Julian Ibarz
7. Sergey Levine

In Surgery

1. Vladimir I. Iglovikov
2. Alexey A. Shvets

Citations in the corpus

• Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones (2020, methodology): “We then evaluate Recovery RL on an image-based obstacle avoidance task on the da Vinci Research Kit (dVRK) [20] where the robot must guide its end effector within 2 mm of a target position from two possible starting locations without touching red 3D printed obstacles in the workspace.”
• Deep Imitation Learning of Sequential Fabric Smoothing Policies (2019, methodology): “Using color and camera domain randomization [39], [49], learned policies are evaluated in simulation and in physical experiments with the da Vinci Research Kit (dVRK) surgical robot [15].”
• 2017 Robotic Instrument Segmentation Challenge (2019, background): “However, despite heavy usage of this dataset in subsequent publications [7]–[9], [11], [12] there are significant limitations due to the limited background variability and also misalignments due to the ground truth being generated by forward kinematics of the da Vinci Research Kit (dVRK) [13] which ”
• SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards (2018, methodology): “We evaluate SWIRL using two simulated reinforcement learning benchmarks and two deformable manipulation tasks on the da Vinci surgical robot (Kazanzides et al., 2014).”
• Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning (2017, methodology): “We interface with the dVRK using open-source electronics and software developed by [15].”