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Surgical/hamlyn/needle_grasp_and_handover/Readme-Needle Grasp and Handover.md

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+<!--
+Open-H Embodiment Dataset README Template (v1.0)
+Hamlyn Centre dVRK Dataset Submission
+-->
+
+# Hamlyn-dVRK: Table-Top Needle Grasp and Handover - README
+
+---
+
+## At a Glance
+
+High-fidelity teleoperated demonstrations of a bimanual **da Vinci Research Kit (dVRK)** robot performing **needle grasping and handover** on a **table-top setup**. The robot uses DeBakey forceps (left) and a needle driver (right) to pick up a surgical needle, optionally re-orient it in-hand using bimanual coordination, and hold it in a suturing-ready pose.
+
+
+---
+
+## Dataset Overview
+
+This dataset contains teleoperated trajectories of trained operators using the dVRK to acquire and prepare a needle for suturing on a table-top scene. The goal is to end with the needle held by the right hand in a stable, suturing-ready pose.
+
+**Task Logic & Execution:**
+The operator utilises the **Patient Side Manipulators (PSMs)** equipped with DeBakey forceps (left) and a needle driver (right).
+*   **Case A (Direct Right Grasp):** If the needle is in a suitable pose and location for the right hand, the right tool directly grasps the needle and moves it to a suturing-ready pose.
+*   **Case B (Left Assist + Re-Orientation):** If direct right grasp is unsuitable, the left tool first grasps the needle. The right tool then cooperates with the left in mid-air to adjust the needle pose, followed by a transfer to the right tool. The episode ends with the needle held by the right hand in a suturing-ready pose.
+
+**Key Features:**
+*   **Pose-Dependent Strategy Selection:** Demonstrations include both direct grasp and assisted re-orientation strategies depending on the initial needle pose.
+*   **Bimanual Mid-Air Manipulation:** Rich coordination signals for controlled needle handovers and in-hand pose adjustment.
+
+| | |
+| :--- | :--- |
+| **Total Trajectories** | `137` |
+| **Total Hours** | `0.245` |
+| **Data Type** | `[ ] Clinical` `[ ] Ex-Vivo` `[x] 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 **Needle Grasp and Handover**.
+
+*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)` <br> `[x] Intermediate (e.g., Trained Researcher)` <br> `[x] Novice (e.g., ML Researcher with minimal experience)` <br> `[ ] 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 needle grasp and handover task, failure modes typically involve an unsuccessful grasp, needle drop. In these instances, the operator does not abort the episode but performs a **recovery manoeuvre**: re-grasping the needle, adjusting the grasp point and tool orientation, and repeating the re-orientation and transfer steps to reach a suturing-ready pose.
+
+
+---
+
+## Diversity Dimensions
+
+*Check all dimensions that were intentionally varied during data collection.*
+
+- [ ] **Camera Position / Angle**
+- [ ] **Lighting Conditions**
+- [x] **Target Object** (Varied initial needle pose)
+- [x] **Spatial Layout** (Varied needle placement in workspace)
+- [ ] **Robot Embodiment**
+- [x] **Task Execution** (Left vs. Right hand dominance / Approach angle)
+- [ ] **Background / Scene**
+- [x] **Initial Robot Configuration** (Table-top domain randomisation)
+
+*Elaboration on Diversity:*
+
+*   **Target Object:** A surgical needle is used, with varied initial orientation and grasp affordances.
+*   **Spatial Layout:** The needle is placed at varied poses within the workspace to elicit either direct right-hand grasping or left-assisted re-orientation.
+*   **Task Execution:** Operators vary grasp points, mid-air re-orientation tactics, and transfer timing to achieve a stable final pose.
+*   **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.  **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)** | <pre><code>@misc{hamlyn_dvrk_openh_2026,<br>  author = {Su, Jianhao and Deng, Kaizhong and Hu, Zhaoyang Jacopo and Rodriguez y Baena, Ferdinando and Giannarou, Stamatia},<br>  title = {Hamlyn dVRK Surgical Manipulation Dataset for Open-H},<br>  year = {2026},<br>  publisher = {Open-H-Embodiment},<br>  note = {Table-Top Domain: Needle Grasp and Handover}<br>}</code></pre> |
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Surgical/hamlyn/needle_grasp_and_handover/meta/info.json

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Surgical/hamlyn/needle_grasp_and_handover/meta/modality.json

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Surgical/hamlyn/needle_grasp_and_handover/meta/relative_stats.json

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Surgical/hamlyn/needle_grasp_and_handover/meta/stats.json

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Surgical/hamlyn/needle_grasp_and_handover/meta/tasks.jsonl

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+{"task_index": 0, "task": "Using the right hand instrument to grasp the needle from the table."}
+{"task_index": 1, "task": "Use the left-hand to grasp the needle from the table, then transfer the needle to the right hand."}

Surgical/hamlyn/needle_grasp_and_handover/t5_embeddings.pkl

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+oid sha256:3e006a55080b7b266b5390c0deb9418d49279037fbfc157083794846a814454c
+size 2098047

Surgical/hamlyn/suturing_2/Readme-Suturing Dual Loop.md

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+<!--
+Open-H Embodiment Dataset README Template (v1.0)
+Hamlyn Centre dVRK Dataset Submission
+-->
+
+# 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.*
+
+- [x] **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?*
+
+- [x] **Human Teleoperation**
+- [ ] **Programmatic/State-Machine**
+- [ ] **AI Policy / Autonomous**
+- [ ] **Other**
+
+
+### Operator Details
+
+| | Description |
+| :--- | :--- |
+| **Operator Count** | 2 |
+| **Operator Skill Level** | `[ ] Expert (e.g., Surgeon, Sonographer)` <br> `[x] Intermediate (e.g., Trained Researcher)` <br> `[x] Novice (e.g., ML Researcher with minimal experience)` <br> `[ ] 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 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**
+- [x] **Target Object** (Varied tissue geometry and stitch locations)
+- [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 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:**
+- [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.  **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)** | <pre><code>@misc{hamlyn_dvrk_openh_2026,<br>  author = {Su, Jianhao and Deng, Kaizhong and Hu, Zhaoyang Jacopo and Rodriguez y Baena, Ferdinando and Giannarou, Stamatia},<br>  title = {Hamlyn dVRK Surgical Manipulation Dataset for Open-H},<br>  year = {2026},<br>  publisher = {Open-H-Embodiment},<br>  note = {Ex-Vivo Domain: Suturing (Dual Loop)}<br>}</code></pre> |
+

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Surgical/hamlyn/suturing_2/meta/info.json

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Surgical/hamlyn/suturing_2/meta/modality.json

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Surgical/hamlyn/suturing_2/meta/relative_stats.json

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Surgical/hamlyn/suturing_2/meta/stats.json

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Surgical/hamlyn/suturing_2/meta/tasks.jsonl

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