SurgSync: Time-Synchronized Multi-Modal Data Collection Framework and Dataset for Surgical Robotics

• URL: http://arxiv.org/abs/2603.06919v1
• Date: Fri, 06 Mar 2026 22:38:45 GMT
• Title: SurgSync: Time-Synchronized Multi-Modal Data Collection Framework and Dataset for Surgical Robotics
• Abstract summary: SurgSync is a multi-modal data collection framework with offline and online synchronization to support training and real-time inference.<n>The framework is implemented on a da Vinci Research Kit (dVRK)
• Score: 10.525174085495314
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Most existing robotic surgery systems adopt a human-in-the-loop paradigm, often with the surgeon directly teleoperating the robotic system. Adding intelligence to these robots would enable higher-level control, such as supervised autonomy or even full autonomy. However, artificial intelligence (AI) requires large amounts of training data, which is currently lacking. This work proposes SurgSync, a multi-modal data collection framework with offline and online synchronization to support training and real-time inference, respectively. The framework is implemented on a da Vinci Research Kit (dVRK) and introduces (1) dual-mode (online/offline-matching) synchronized recorders, (2) a modern stereo endoscope to achieve image quality on par with clinical systems, and (3) additional sensors such as a side-view camera and a novel capacitive contact sensor to provide ground truth contact data. The framework also incorporates a post-processing toolbox for tasks such as depth estimation, optical flow, and a practical kinematic reprojection method using Gaussian heatmap. User studies with participants of varying skill levels are performed with ex-vivo tissue to provide clinically realistic data, and a network for surgical skill assessment is employed to demonstrate utilization of the collected data. Through the user study experiments, we obtained a dataset of 214 validated instances across multiple canonical training tasks. All software and data are available at surgsync.github.io.

Related papers

• RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation [48.828787848144806]
  We introduce digital teleoperation, a paradigm that decouples data collection from physical constraints by replacing real robot with a generative world model.<n>We instantiate this paradigm in RynnWorld-Teleop, a system that integrates depth-aware skeletal conditioning, progressive human-to-robot training on a video Diffusion Transformer, and streaming autoregressive distillation.<n>This pipeline compresses the generative process into a single-pass inference, enabling 40+ FPS, real-time interactive generation on a single H100 GPU.
  arXiv  Detail & Related papers  (2026-07-07T17:58:11Z)
• MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery [4.287077788799387]
  MiDAS is a platform-agnostic system for time-synchronized, non-invasive multimodal data acquisition across surgical robotic platforms.<n>We validated MiDAS on the open-source Raven-II and the clinical da Vinci Xi by collecting multimodal datasets of peg transfer and hernia repair suturing tasks.
  arXiv  Detail & Related papers  (2026-02-12T20:56:15Z)
• Synthetic Dataset Generation for Autonomous Mobile Robots Using 3D Gaussian Splatting for Vision Training [0.708987965338602]
  We propose a novel method for automatically generating annotated synthetic data in Unreal Engine.<n>We demonstrate that synthetic datasets can achieve performance comparable to that of real-world datasets.<n>This is the first application of synthetic data for training object detection algorithms in robot soccer.
  arXiv  Detail & Related papers  (2025-06-05T14:37:40Z)
• dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale [9.732963094538837]
  We introduce dARt Vinci, a scalable data collection platform for robot learning in surgical settings.<n>The system uses Augmented Reality (AR) hand tracking and a high-fidelity physics engine to capture subtle maneuvers in primitive surgical tasks.
  arXiv  Detail & Related papers  (2025-03-07T18:07:54Z)
• OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis [55.390060529534644]
  We propose OS-Genesis, a novel data synthesis pipeline for Graphical User Interface (GUI) agents.<n>Instead of relying on pre-defined tasks, OS-Genesis enables agents first to perceive environments and perform step-wise interactions.<n>We demonstrate that training GUI agents with OS-Genesis significantly improves their performance on highly challenging online benchmarks.
  arXiv  Detail & Related papers  (2024-12-27T16:21:58Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Dialogue-Contextualized Re-ranking for Medical History-Taking [5.039849340960835]
  We present a two-stage re-ranking approach that helps close the training-inference gap by re-ranking the first-stage question candidates. We find that relative to the expert system, the best performance is achieved by our proposed global re-ranker with a transformer backbone.
  arXiv  Detail & Related papers  (2023-04-04T17:31:32Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments [12.019996672009375]
  We show that creating realistic simulated images from human models is a viable alternative to large-scale in situ data collection. Because synthetic generation of training data from human-based models scales easily, we find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real data-trained models.
  arXiv  Detail & Related papers  (2022-06-13T13:08:41Z)
• Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots [129.46920552019247]
  We propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view. We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy.
  arXiv  Detail & Related papers  (2021-02-09T10:34:32Z)
• Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery [84.73764603474413]
  We propose a novel online approach of multi-modal graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information. The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset.
  arXiv  Detail & Related papers  (2020-11-03T11:00:10Z)

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.