Surgical/hamlyn/peg_transfer/README.md

<!--
Open-H Embodiment Dataset README Template (v1.0)
Hamlyn Centre dVRK Dataset Submission
-->

# Hamlyn-dVRK: Table-Top Peg Transfer - README

---

## At a Glance

High-fidelity teleoperated demonstrations of a bimanual **da Vinci Research Kit (dVRK)** robot performing **peg transfer** on a **table-top training rig**. The robot uses DeBakey forceps (left) and a needle driver (right) to pick up pegs, perform a handover between tools, and place pegs onto specified posts.


---

## Dataset Overview

This dataset contains teleoperated trajectories of trained operators using the dVRK to perform peg transfer on a table-top training apparatus. The goal is to pick up a peg with the left hand, transfer it to the right hand, and place it accurately onto a target post according to a given instruction.

**Task Logic & Execution:**
The operator utilises the **Patient Side Manipulators (PSMs)** equipped with DeBakey forceps (left) and a needle driver (right).
*   **Pick (Left Hand):** The left hand grasps the instructed peg.
*   **Handover + Place (Right Hand):** The peg is transferred to the right hand and placed onto a target post on the board.

**Key Features:**
*   **Table-Top Dexterity Primitive:** Clean visual scene with rigid objects, supporting accurate benchmarking of bimanual handovers and placement.
*   **Instruction-Conditioned Placement:** Demonstrations follow a given instruction specifying which peg to move and where to place it.

| | |
| :--- | :--- |
| **Total Trajectories** | `317` |
| **Total Hours** | `0.545` |
| **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 **Peg Transfer**.

*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 peg transfer task, failure modes typically involve dropping the peg during transport, an unstable handover, or misplacement onto the post. In these instances, the operator does not abort the episode but performs a **recovery manoeuvre**: re-grasping the peg, re-aligning the approach, and repeating the handover and placement steps.


---

## Diversity Dimensions

*Check all dimensions that were intentionally varied during data collection.*

- [ ] **Camera Position / Angle**
- [ ] **Lighting Conditions**
- [x] **Target Object** (Varied peg and target post)
- [x] **Spatial Layout** (Varied board pose / peg arrangement)
- [ ] **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 table-top peg board was used. Diversity is achieved through varying which peg is selected and which post is the placement target.
*   **Spatial Layout:** The board pose and/or peg arrangement are varied between sets to reduce overfitting to absolute coordinates.
*   **Task Execution:** Operators vary approach angle, grasp point, and handover strategy to maintain stable transport and accurate placement.
*   **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: Peg Transfer}<br>}</code></pre> |