An Open-Source Robotics Research Platform for Autonomous Laparoscopic Surgery

TL;DR

An open-source, robot-agnostic Remote Center of Motion (RCM) controller based on a closed-form analytical velocity solver that enforces the trocar constraint deterministically without iterative optimization is presented.

Abstract

Autonomous robot-assisted surgery demands reliable, high-precision platforms that strictly adhere to the safety and kinematic constraints of minimally invasive procedures. Existing research platforms, primarily based on the da Vinci Research Kit, suffer from cable-driven mechanical limitations that degrade state-space consistency and hinder the downstream training of reliable autonomous policies. We present an open-source, robot-agnostic Remote Center of Motion (RCM) controller based on a closed-form analytical velocity solver that enforces the trocar constraint deterministically without iterative optimization. The controller operates in Cartesian space, enabling any industrial manipulator to function as a surgical robot. We provide implementations for the UR5e and Franka Emika Panda manipulators, and integrate stereoscopic 3D perception. We integrate the robot control into a full-stack ROS-based surgical robotics platform supporting teleoperation, demonstration recording, and deployment of learned policies via a decoupled server-client architecture. We validate the system on a bowel grasping and retraction task across phantom, ex vivo, and in vivo porcine laparoscopic procedures. RCM deviations remain sub-millimeter across all conditions, and trajectory smoothness metrics (SPARC, LDLJ) are comparable to expert demonstrations from the JIGSAWS benchmark recorded on the da Vinci system. These results demonstrate that the platform provides the precision and robustness required for teleoperation, data collection and autonomous policy deployment in realistic surgical scenarios.

Figures (7)

• Figure 1: System overview of the platform: the teleoperation interface consists of a haptic input device and a clutch pedal board, combined with a Meta Quest headset providing immersive 3D perception. On the patient side, the hardware setup features two UR5e robotic arms, one for controlling the surgical instrument and the other equipped with a stereoscopic endoscope to capture the surgical workspace.
• Figure 2: The platform is built with modularity and scalability at its core, with inter-component communication abstracted through ROS topics to remain hardware-agnostic. This architecture allows input devices and robot actuators to be readily exchanged, and new modalities can be incorporated by simply implementing the corresponding nodes or C++ modules. In teleoperation mode, the operator's input device supplies target position commands to the robot controller, while stereoscopic 3D visualization is rendered for the operator through the Meta Quest headset. During policy deployment, the control signal is instead generated by the autonomous policy, which interfaces with the ROS ecosystem through a ZMQ-based server–client architecture, keeping model inference decoupled from the broader system. meta_quest3ur_ur5e
• Figure 3: Remote-Center-of-Motion instrument control showing spherical coordinates (pitch, yaw, roll, translation) and Cartesian tip velocity methods, with all vectors expressed in the instrument robot's base frame.
• Figure 4: Immersive stereoscopic visualization through the Meta Quest headset running Endomersion mats2025endomersion showing the surgical workspace captured by the endoscope. Only the left eye view is shown; the right eye receives the corresponding stereo pair to enable depth perception.
• Figure 5: Representative frames from datasets collected using the platform: (a) Needle Driving, (b) Pick and Place, (c) Thread in Hole, (d) Endoscope Guidance, and Bowel Grasping and Retraction (BGR) across (e) phantom, (f) ex vivo, and (g) in vivo settings.