An Open-Source Robotics Research Platform for Autonomous Laparoscopic Surgery

TL;DR

This paper introduces an open-source, robot-agnostic platform for autonomous laparoscopic surgery that ensures high precision and safety, overcoming limitations of existing systems and enabling reliable autonomous policies.

Contribution

The authors present a novel, analytical RCM controller compatible with industrial robots, integrated into a full ROS-based surgical platform supporting teleoperation and autonomous policy deployment.

Findings

RCM deviations are sub-millimeter across all tested conditions.

Trajectory smoothness is comparable to expert demonstrations on the da Vinci system.

The platform successfully performs bowel grasping and retraction in various surgical scenarios.

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.