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Hamlyn-dVRK: Ex-Vivo Suturing (Dual Loop) - README

At a Glance

High-fidelity teleoperated demonstrations of a bimanual da Vinci Research Kit (dVRK) robot performing suturing (dual loop) on ex-vivo porcine tissue. This task captures a continuous, multi-step suturing sequence with repeated needle passes and bimanual handovers using DeBakey forceps (left) and a needle driver (right).

Dataset Overview

This dataset contains teleoperated trajectories of trained operators using the dVRK to perform a dual-loop (continuous) suturing sequence on ex-vivo animal tissue. The goal is to complete two consecutive needle passes through tissue, including all intermediate steps needed to keep the needle in a suturing-ready pose.

Task Logic & Execution: The operator utilises the Patient Side Manipulators (PSMs) equipped with DeBakey forceps (left) and a needle driver (right).

  • First Stitch: The right hand drives the needle through tissue; the left hand grasps and extracts the needle.
  • Needle Return + Second Stitch: The left hand transfers the needle back to the right hand in a suturing-ready pose, then the right hand performs a second pass through tissue followed by left-hand extraction.

Key Features:

  • Real Ex-Vivo Interaction: Uses porcine tissue to capture compliance and deformation during repeated needle passes.
  • Continuous Multi-Step Sequence: Demonstrations include bimanual extraction, mid-air re-orientation, handover back to the right, and a second stitch without resetting the episode.
Total Trajectories 186
Total Hours 1.189
Data Type [ ] Clinical [x] Ex-Vivo [ ] Table-Top Phantom [ ] Digital Simulation [ ] Physical Simulation [ ] Other
License CC BY 4.0
Version 1.0

Hamlyn dVRK Dataset Overview

This is part of the Hamlyn dVRK Dataset, which encompasses six distinct surgical tasks. All data were collected through teleoperation on bimanual tasks performed on ex-vivo animal samples and phantoms.

Task Conditioning

The tasks are categorized into two types:

  • Non-conditioned: Single sentence task descriptions
  • Language-conditioned: Multiple variants of task descriptions across episodes

Dataset Statistics

  • Total Episodes: 972
  • Total Frames: 545k @ 30Hz
  • Dataset Duration: 5.04 hours

Episode Outcomes

Outcome Category Episodes Description
Success 780 Task completed successfully with specified conditions
Recovery 110 Task finished with recovery behavior
Failed 82 Task failed to complete

*Success rates vary across tasks due to varying difficulty levels for operators during teleoperation.

Tasks & Domain

Domain

Select the primary domain for this dataset.

  • Surgical Robotics
  • Ultrasound Robotics
  • Other Healthcare Robotics

Demonstrated Skills

This specific dataset subset focuses on Suturing (Dual Loop).

Note: This is part of the Hamlyn dVRK Data Collection which also includes:

  • Tissue Retraction / Exposure
  • Knot Tying
  • Suturing (Single Loop)
  • Suturing (Dual Loop)
  • Peg Transfer
  • Needle Grasp and Handover

Data Collection Details

Collection Method

How was the data collected?

  • Human Teleoperation
  • Programmatic/State-Machine
  • AI Policy / Autonomous
  • Other

Operator Details

Description
Operator Count 2
Operator Skill Level [ ] Expert (e.g., Surgeon, Sonographer)
[x] Intermediate (e.g., Trained Researcher)
[x] Novice (e.g., ML Researcher with minimal experience)
[ ] N/A
Collection Period From 2025-12-01 to 2026-01-15
Input Interface Teleoperation interface with bimanual Force Dimension Sigma 7 haptic device

Recovery Demonstrations

Does this dataset include examples of recovering from failure?

  • Yes
  • No

If yes, please briefly describe the recovery process:

In the dual-loop suturing task, failure modes typically involve an incorrect bite, needle drop, loss of needle control during transfer, or unstable tissue interaction. In these instances, the operator does not abort the episode but performs a recovery manoeuvre: re-grasping and re-orienting the needle, re-aligning the approach, and repeating the necessary intermediate steps before continuing the sequence.

Diversity Dimensions

Check all dimensions that were intentionally varied during data collection.

  • Camera Position / Angle
  • Lighting Conditions
  • Target Object (Varied tissue geometry and stitch locations)
  • Spatial Layout (Randomised tissue placement)
  • Robot Embodiment
  • Task Execution (Left vs. Right hand dominance / Approach angle)
  • Background / Scene
  • Initial Robot Configuration (Ex-Vivo domain randomisation)

Elaboration on Diversity:

  • Target Object: Fresh porcine tissue was used to capture deformation during repeated needle driving. Diversity is achieved through variations in tissue stiffness/geometry and stitch locations for the two consecutive passes.
  • Spatial Layout: The tissue pose was manually randomised within the workspace between sets to reduce overfitting to absolute coordinates.
  • Task Execution: Operators vary entry angle, transfer strategy, and re-orientation in mid-air to keep the needle in a stable pose across the sequence.
  • Initial Robot Configuration: End-effectors were reset to a randomised "home" position above the workspace before each trajectory.

🛠️ Equipment & Setup

Robotic Platform(s)

List the primary robot(s) used.

  • Robot 1: dVRK (da Vinci Research Kit) - Patient Side Manipulators (PSM1, PSM2)

Sensors & Cameras

List the sensors and cameras used. Specify model names where possible.

Type Model/Details
Primary Camera Intel RealSense D405 (RGB + Depth), 848x480 @ 30fps
Wrist Camera INSKAM 5.5mm USB Endo-Cam (Right/Left Arm) with custom built mount, view angle 66 degree, focal length 4-10cm, 640x480 @ 24fps
Kinematics dVRK High-Frequency Joint Encoders (100Hz)

🎯 Action & State Space Representation

The dataset follows the standard LeRobot format for bimanual manipulation.

Action Space Representation

Primary Action Representation:

  • Absolute Cartesian (position/orientation relative to robot base)
  • Relative Cartesian
  • Joint Space

Orientation Representation:

  • Quaternions (x, y, z, w)
  • Euler Angles
  • Rotation Matrix

Reference Frame:

  • Robot Base Frame (Base of each PSM arm)
  • Camera Frame

Action Dimensions:

action: [x, y, z, qx, qy, qz, qw, gripper_angle, x, y, z, qx, qy, qz, qw, gripper_angle]

  • The first 8 dimensions are for the left arm and last 8 dimensions are for the right arm
  • x, y, z: Absolute position in PSM base frame (meters)
  • qx, qy, qz, qw: Absolute orientation as quaternion
  • gripper_angle: Normalized jaw opening angle (1 = opened, 0 = closed)

State Space Representation

State Information Included:

  • Joint Positions
  • Joint Velocities
  • End-Effector Pose (No extra end-effector used with dVRK)
  • Gripper State

Primary State:

observation.state: [x, y, z, qx, qy, qz, qw, gripper_angle, x, y, z, qx, qy, qz, qw, gripper_angle]

  • The first 8 dimensions are for the left arm and last 8 dimensions are for the right arm
  • x, y, z: Absolute position in PSM base frame (meters)
  • qx, qy, qz, qw: Absolute orientation as quaternion
  • gripper_angle: Normalized jaw opening angle (1 = opened, 0 = closed)

State Dimensions:

observation.state.left_arm_cartesian: [x, y, z, qx, qy, qz, qw, gripper_angle]

observation.state.right_arm_cartesian: [x, y, z, qx, qy, qz, qw, gripper_angle]

observation.state.left_arm_joint: [j1, j2, j3, j4, j5, j6, gripper_angle]

observation.state.right_arm_joint: [j1, j2, j3, j4, j5, j6, gripper_angle]

  • j1-j6: Joint positions for 6-DOF PSM arm (radians)
  • gripper_angle: Normalized jaw opening angle (1 = opened, 0 = closed)

⏱️ Data Synchronization Approach

Describe how you achieved proper data synchronization.

Distributed Synchronization Architecture: The data collection setup involves a distributed system with a robot control machine that teleop the dVRK and a sensor recording machine that stream kinematics, and videos from camers.

  1. Clock Sync: We utilize Chrony (NTP) to enforce strict time alignment between the two workstations, minimizing clock skew to < 3.3 ms.
  2. Timestamping: All data streams (camera RGB and depth images, wrist camer images, robot kinematics) are timestamped with ROS wall-time headers at the exact moment of capture.
  3. Delay Compensattion and Trimming: The different cameras can introduce small relative delays. These delays are first estimated and compensated by applying a common temporal offset to each stream. Subsequently, all streams are temporally trimmed to their common interval, bounded by the latest start time and the earliest end time across all modalities.
  4. Alignment: During the LeRobot conversion process, frames from all data streams are aligned to a reference timeline at 30 Hz30 Hz. This is achieved via nearest-neighbor temporal interpolation based on the synchronized ROS timestamps.

Attribution & Contact

Please provide attribution for the dataset creators and a point of contact.

Dataset Lead Jianhao Su, Kaizhong Deng, Zhaoyang Jacopo Hu
Institution The Hamlyn Centre for Robotic Surgery, Imperial College London
Contact Email k.deng21@imperial.ac.uk (for technical issues), stamatia.giannarou@imperial.ac.uk (for correspondence)
Citation (BibTeX) 
@misc{hamlyn_dvrk_openh_2026,
  author = {Su, Jianhao and Deng, Kaizhong and Hu, Zhaoyang Jacopo and Rodriguez y Baena, Ferdinando and Giannarou, Stamatia},
  title = {Hamlyn dVRK Surgical Manipulation Dataset for Open-H},
  year = {2026},
  publisher = {Open-H-Embodiment},
  note = {Ex-Vivo Domain: Suturing (Dual Loop)}
}