Autonomous Laparoscope Control through Unified Mechanics-Based Representation of Multimodal Intraoperative Information
- URL: http://arxiv.org/abs/2605.04408v1
- Date: Wed, 06 May 2026 02:01:41 GMT
- Title: Autonomous Laparoscope Control through Unified Mechanics-Based Representation of Multimodal Intraoperative Information
- Abstract summary: Laparoscope-holding robots can provide surgeons with a stable laparoscopic field of view (FOV) and reduce the burden on human assistants.<n> multimodal signals, such as position, force/torque, and images, differ markedly in physical meaning and units.<n>We propose a laparoscope-holding robot control method based on unified mechanics modeling of multimodal information.
- Score: 13.527135413058856
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Laparoscope-holding robots can provide surgeons with a stable laparoscopic field of view (FOV) and reduce the burden on human assistants. To maintain an ideal intraoperative FOV, the robot must continuously adjust the laparoscope pose according to intraoperative information. However, intraoperative multimodal signals, such as position, force/torque, and images, differ markedly in physical meaning and units, making it difficult to build a unified representation and to generate control commands that can be used directly for laparoscope control. To address this issue, we propose a laparoscope-holding robot control method based on unified mechanics modeling of multimodal information. First, we design mapping strategies for multiple intraoperative sources, including position, force/torque, and images, and unify them into an equivalent-wrench representation in the operational space. Then, using a task-priority scheme, we inject the wrenches into the task space and the null space, respectively, and synthesize laparoscope control commands via task-priority projection, thereby achieving consistent representation and coordinated fusion of multimodal information within a single framework. Finally, taking the intraoperative remote center of motion (RCM) position, force/torque sensor readings, and laparoscopic images as examples, we construct an RCM-constraint wrench to enforce the RCM geometric constraint and reduce the contact force at the trocar site, a laparoscope-manipulation wrench to enable compliant dragging, and an instrument-tracking wrench to achieve autonomous visual tracking of the instruments. Experiments on a surgical phantom and in vivo porcine trials demonstrate that the proposed method supports multi-task operation, including compliant laparoscope manipulation and autonomous instrument tracking, while maintaining the RCM constraint and reducing sustained trocar-site loading.
Related papers
- SurgLAT: Surgical Latent Attention Tracking for Depth-Aware Robotic Laparoscope Control [32.598197128759146]
We present Surgical Latent Attention Tracking (SurgLAT), a causal online framework for latent surgical attention modeling and autonomous laparoscopic view control.<n>SurgLAT uses a frozen DINOv3 encoder and a state-conditioned spatial token mixer to extract operative evidence under a memory-guided spatial prior.<n>We further introduce a robotic deployment framework with explicit laparoscopic Remote Center of Motion (RCM) constrained control based on virtual-axis formulation.
arXiv Detail & Related papers (2026-08-08T03:04:01Z) - OpenRC: An Open-Source Robotic Colonoscopy Framework for Multimodal Data Acquisition and Autonomy Research [36.65725257567292]
We present OpenRC, an open-source modular robotic colonoscopy framework.<n>It supports simultaneous recording of video, operator commands, actuation state, and distal tip pose.<n>We collected a multimodal dataset comprising 1,894 teleoperated episodes 19 hours across 10 structured task variations.
arXiv Detail & Related papers (2026-04-04T16:07:33Z) - An Open-Source Robotics Research Platform for Autonomous Laparoscopic Surgery [1.801618662867143]
We present an open-source, robot-agnostic Remote Center of Motion (RCM) controller based on a closed-form analytical velocity solver.<n>The controller operates in Cartesian space, enabling any industrial manipulator to function as a surgical robot.<n>We integrate the robot control into a full-stack ROS-based surgical robotics platform supporting teleoperation, demonstration recording, and deployment of learned policies.
arXiv Detail & Related papers (2026-03-09T15:26:27Z) - Learning Surgical Robotic Manipulation with 3D Spatial Priors [73.00031539525202]
We introduce the Spatial Surgical Transformer (SST), an end-to-end visuomotor policy that empowers surgical robots with 3D spatial awareness.<n>Based on Surgical3D, we finetune a powerful geometric transformer to extract robust 3D latent representations from stereo endoscopes images.<n>SST achieves state-of-the-art performance and strong spatial generalization on complex surgical tasks.
arXiv Detail & Related papers (2026-03-04T07:19:56Z) - ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation [55.467742403416175]
We introduce a physics-driven neural algorithm that translates large-scale motion capture to humanoid embodiments.<n>We learn a unified multimodal controller that supports both dense references and sparse task specifications.<n>Results show that ULTRA generalizes to autonomous, goal-conditioned whole-body loco-manipulation from egocentric perception.
arXiv Detail & Related papers (2026-03-03T18:59:29Z) - Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration [58.4036440289082]
Hand-object motion-capture (MoCap) offer large-scale, contact-rich demonstrations and hold promise for dexterous robotic scopes.<n>We introduce Dexplore, a unified single-loop optimization that performs repositories and tracking to learn robot control policies directly from MoCap at scale.
arXiv Detail & Related papers (2025-09-11T17:59:07Z) - Open-Source Multi-Viewpoint Surgical Telerobotics [6.763309989418208]
We conjecture that introducing one or more adjustable viewpoints in the abdominal cavity would unlock novel visualization and collaboration strategies for surgeons.<n>We are building a synchronized multi-viewpoint, multi-sensor robotic surgery system by integrating high-performance vision components and upgrading the da Vinci Research Kit control logic.
arXiv Detail & Related papers (2025-05-16T11:41:27Z) - Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery [18.64205729932939]
We present an implementation strategy for a robot that performs peg transfer tasks in Fundamentals of Laparoscopic Surgery (FLS) via imitation learning.
In this study, we achieve more accurate imitation learning with only monocular images.
We implemented an overall system using two Franka Emika Panda Robot Arms and validated its effectiveness.
arXiv Detail & Related papers (2024-05-06T13:12:25Z) - Multi-Scale Control Signal-Aware Transformer for Motion Synthesis
without Phase [72.01862340497314]
We propose a task-agnostic deep learning method, namely Multi-scale Control Signal-aware Transformer (MCS-T)
MCS-T is able to successfully generate motions comparable to those generated by the methods using auxiliary information.
arXiv Detail & Related papers (2023-03-03T02:56:44Z) - Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle
Activity for Enhanced Myoelectric Control of Hand Prostheses [78.120734120667]
We introduce a novel method, based on a long short-term memory (LSTM) network, to continuously map forearm EMG activity onto hand kinematics.
Ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset.
Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.
arXiv Detail & Related papers (2021-04-29T00:11:32Z) - Domain Adaptive Robotic Gesture Recognition with Unsupervised
Kinematic-Visual Data Alignment [60.31418655784291]
We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot.
It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture.
Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
arXiv Detail & Related papers (2021-03-06T09:10:03Z) - Real-time Surgical Environment Enhancement for Robot-Assisted Minimally
Invasive Surgery Based on Super-Resolution [18.696539908774454]
We propose a Generative Adversarial Network (GAN)-based video super-resolution method to construct a framework for automatic zooming ratio adjustment.
It can provide automatic real-time zooming for high-quality visualization of the Region Of Interest (ROI) during the surgical operation.
arXiv Detail & Related papers (2020-11-08T15:40:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.