diff --git a/Surgical/hamlyn/knot_tying/Readme-Knot Tying.md b/Surgical/hamlyn/knot_tying/Readme-Knot Tying.md new file mode 100644 index 0000000000000000000000000000000000000000..e18c005ffb02ba11ea19b3a89bc8a2af1e469b6c --- /dev/null +++ b/Surgical/hamlyn/knot_tying/Readme-Knot Tying.md @@ -0,0 +1,256 @@ + + +# Hamlyn-dVRK: Ex-Vivo Knot Tying - README + +--- + +## At a Glance + +High-fidelity teleoperated demonstrations of a bimanual **da Vinci Research Kit (dVRK)** robot performing **knot tying** on **ex-vivo porcine tissue**. The setup uses DeBakey forceps (left) and a needle driver (right) to manipulate suture strands and form stable surgical knots on deformable, wet tissue. + + +--- + +## Dataset Overview + +This dataset contains teleoperated trajectories of trained operators using the dVRK to perform knot tying on ex-vivo animal tissue. The goal is to reliably form a stable knot while maintaining appropriate strand tension and avoiding strand slip on wet tissue. + +**Task Logic & Execution:** +The operator utilises the **Patient Side Manipulators (PSMs)** equipped with DeBakey forceps (left) and a needle driver (right). +* **Initial Condition:** The needle has already been passed through the tissue slit, and both suture ends are visible outside the tissue. +* **Knot Tying:** The robot grasps and manipulates the two strand ends bimanually to create the required crossings and throws, then tightens the knot while controlling tension and strand alignment. + +**Key Features:** +* **Pre-Threaded Start State:** The needle is already passed through the tissue slit and both suture ends are outside the tissue at the start of each trajectory, isolating knot tying from needle driving. +* **Bimanual Throw Formation and Tightening:** Demonstrations capture coordinated grasping, crossing, and controlled tightening required to form consistent knot throws on ex-vivo porcine tissue. + +| | | +| :--- | :--- | +| **Total Trajectories** | `77` | +| **Total Hours** | `0.254` | +| **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.* + +- [x] **Surgical Robotics** +- [ ] **Ultrasound Robotics** +- [ ] **Other Healthcare Robotics** + +### Demonstrated Skills + +This specific dataset subset focuses on **Knot Tying**. + +*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?* + +- [x] **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?* + +- [x] **Yes** +- [ ] **No** + +**If yes, please briefly describe the recovery process:** + +In the knot tying task, failure modes typically involve losing strand tension, or an unstable throw formation. In these instances, the operator does not abort the episode but performs a **recovery manoeuvre**: re-grasping one or both strands, re-establishing tension, and repeating the throw and tightening steps to complete the knot. + + +--- + +## Diversity Dimensions + +*Check all dimensions that were intentionally varied during data collection.* + +- [ ] **Camera Position / Angle** +- [ ] **Lighting Conditions** +- [x] **Target Object** (Varied tissue geometry and suture configuration) +- [x] **Spatial Layout** (Randomised tissue placement) +- [ ] **Robot Embodiment** +- [x] **Task Execution** (Left vs. Right hand dominance / Approach angle) +- [ ] **Background / Scene** +- [x] **Initial Robot Configuration** (Ex-Vivo domain randomisation) + +*Elaboration on Diversity:* + +* **Target Object:** Fresh **porcine tissue** was used to capture wet-surface interaction. Diversity is achieved through variations in tissue stiffness/geometry and suture strand configuration (e.g., visible strand lengths and relative positioning). +* **Spatial Layout:** The tissue pose was manually randomised within the workspace between sets to reduce overfitting to absolute coordinates. +* **Task Execution:** Operators vary grasp points, crossing direction, and tightening strategy to maintain tension and produce stable throws. +* **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:** +- [x] **Absolute Cartesian** (position/orientation relative to robot base) +- [ ] **Relative Cartesian** +- [ ] **Joint Space** + +**Orientation Representation:** +- [x] **Quaternions** (x, y, z, w) +- [ ] **Euler Angles** +- [ ] **Rotation Matrix** + +**Reference Frame:** +- [x] **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:** +- [x] **Joint Positions** +- [x] **Joint Velocities** +- [ ] **End-Effector Pose** (No extra end-effector used with dVRK) +- [x] **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. **Clutch Masking:** As clutch is frequently used in teleoperation, sudden and meaningless breaks in the trajectory were removed to reduce stops during operation to improve trajectory smoothness. +5. **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: Knot Tying}
}
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