An Open-Source Robotics Research Platform for Autonomous Laparoscopic Surgery
Authors: Ariel Rodriguez, Lorenzo Mazza, Martin Lelis, Rayan Younis, Sebastian Bodenstedt, Martin Wagner, Stefanie Speidel
Organizations: Department of Translational Surgical Oncology, NCT/UCC Dresden, a partnership between DKFZ, MF UKD, TUD, HZDR Dresden, Germany · Cluster of Excellence-CeTI, TUD, Germany · Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD, Germany
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.
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Fig. 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. [ 22 , 23 ]
Fig. 4: Immersive stereoscopic visualization through the Meta Quest headset running Endomersion [ 17 ] 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.
Fig. 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.
Fig. 6: Trajectory smoothness comparison using SPARC (left) and Log Dimensionless Jerk (right) metrics, where values closer to zero indicate smoother motion. Our robot testbed produces progressively less smooth trajectories from phantom to in vivo, consistent with increasing difficulty for the Bowel Grasping and Retraction task. All three conditions yield comparable trajectory smoothness metrics to the JIGSAWS Knot Tying dataset recorded on the da Vinci surgical system. All signals downsampled to 5 Hz for fair comparison.
Fig. 7: Remote Center of Motion (RCM) deviation across experimental conditions. Mean deviation (left) and worst-case maximum deviation (right) remain sub-millimeter across all conditions, with phantom ( x~=0.035 mm), ex vivo ( x~=0.052 mm), and in vivo ( x~=0.079 mm) showing a progressive increase consistent with greater tissue interaction complexity.
In robot-assisted laparoscopic minimally invasive surgery (MIS), accurate enforcement of the remote center of motion (RCM) constraint is critical for safe and stable automatic field-of-view (FoV) adjustment. Although control-based RCM strategies are widely adopted due to their flexibility and cost-effectiveness, systematic comparison of different RCM formulations and image-based visual servoing (IBVS) frameworks remains challenging due to the lack of a unified and reproducible benchmark. This paper presents an open-source simulation framework integrating three representative RCM modeling approaches and six IBVS-based control architectures within a unified velocity-level formulation, enabling controlled and consistent evaluation. Through structured case studies, the framework reveals key structural sensitivities arising from modeling and controller interactions, including the impact of tangent-plane definition, constraint dimensionality, open- versus closed-loop enforcement, and robustness near kinematic singularities. All resources are released and demostrations are provided in the supplementary video, providing a reproducible foundation for RCM-constrained visual servoing research.
Jing Zhang, Mengtang Li
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
Autonomous laparoscopic camera control requires continuous understanding of the surgeon's operative intent in dynamic surgical scenes, where the target operative region is not a stable physical object but a latent and temporally evolving attention state. In this work, we present Surgical Latent Attention Tracking (SurgLAT), a causal online framework for latent surgical attention modeling and autonomous laparoscopic view control. SurgLAT uses a frozen DINOv3 encoder and a state-conditioned spatial token mixer to extract operative evidence under a memory-guided spatial prior, while a selective causal latent memory module jointly models short-term motion continuity and long-horizon surgical intent evolution through dynamic retrieval of current, recent, and historical latent states. The learned latent surgical attention state is decoded into a probabilistic attention heatmap and operative region for downstream endoscope guidance. Beyond perception, we further introduce a robotic deployment framework with explicit laparoscopic Remote Center of Motion (RCM) constrained control based on virtual-axis formulation, together with redundancy-aware null-space initialization for stable and smooth manipulator motion. We validate the full system on real laparoscopic surgical videos and a physical robotic laparoscope platform. Experimental results demonstrate robust online operative-region tracking and stable autonomous endoscopy adjustment under occlusion, rapid motion, and target transitions, highlighting the effectiveness of latent surgical intent modeling for surgical autonomy.
Rulin Zhou, Qiujie Song, Yujie Ma +10
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR 999077, China · Shenzhen Loop Area Institute, Shen Zhen 518055, China · College of Mechatronics and Engineering, Shenzhen University, Shenzhen 518060, China +2
Minimally invasive surgery (MIS) reduces patient trauma and shortens recovery time; however, conventional laparoscopic instruments remain constrained by limited range of movements. This work presents the control architecture of a 4-DOF flexible laparoscopic instrument integrating distal bending, independent distal head rotation, shaft rotation, and a gripper, while maintaining a 10 mm diameter compatible with standard trocars. The actuation unit and SpaceMouse teleoperation are implemented on Raspberry Pi 5 with Motoron controllers. An analytical scissor-linkage model is derived and parameterized. The predicted jaw opening corresponds to CAD measurements (MAE 0.13{\textdegree}) and OptiTrack motion capture (MAE 1.43{\textdegree}). Integration with the ATHENA parallel robot is validated through a simulated pancreatic surgery procedure.
Doina Pisla, Ionut Zima, Calin Vaida +7
CESTER · CESTER, Dept. of Mechanical Engineering, Technical University of Cluj-Napoca, European University of Technology, EU, Technical Sciences Academy of Romania, Bucharest, Cluj-Napoca, Romania · CESTER, Dept. of Mechanical Engineering, Technical University of Cluj-Napoca, European University of Technology, EU, Cluj-Napoca, Romania +2