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Ultrasound/cuhk/xiangyu_chu/bmt_insertion_tumor_fishball_dataset/bmt_insertion_tumor_fishball_dataset/meta/README.md

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+<!--
+Open-H Embodiment Dataset README Template (v1.0)
+Please fill out this template and include it in the ./metadata directory of your LeRobot dataset.
+This file helps others understand the context and details of your contribution.
+-->
+
+# [Dataset Name] - README
+
+---
+
+## 📋 At a Glance
+
+Static dual-arm in-plane needle insertion with interferences
+
+---
+
+## 📖 Dataset Overview
+
+*This dataset contains 2000 trajectories of a trained researcher using the dual-arm UR5e to perform needle insertion tasks on ex-vivo tissues include objects: mimicked vessel, different simulated tumors (grape, blueberry and fish ball), pork lean, bone ,liver, kidney.* 
+<!-- It includes successful trials, failures, and recovery attempts to provide a robust dataset for training imitation learning policies.* -->
+
+| | |
+| :--- | :--- |
+| **Total Trajectories** | `[2000]` |
+| **Total Hours** | `[25]` |
+| **Data Type** | `[ ] Clinical` `[x] Ex-Vivo` `[ ] Table-Top Phantom` `[ ] Digital Simulation` `[ ] Physical Simulation` `[ ] Other (If checked, update "Other")` |
+| **License** | CC BY 4.0 |
+| **Version** | `[1.0]` |
+
+---
+
+## 🎯 Tasks & Domain
+
+### Domain
+
+*Select the primary domain for this dataset.*
+
+- [ ] **Surgical Robotics**
+- [x] **Ultrasound Robotics**
+- [ ] **Other Healthcare Robotics** (Please specify: `[Your Domain]`)
+
+### Demonstrated Skills
+
+*Autonomous dual-arm robotic needle insertion.*
+
+<!-- ***Example:***
+- Needle-insertion -->
+
+---
+
+## 🔬 Data Collection Details
+
+### Collection Method
+
+*How was the data collected?*
+
+- [ ] **Human Teleoperation**
+- [x] **Programmatic/State-Machine**
+- [ ] **AI Policy / Autonomous**
+- [ ] **Other** (Please specify: `[Your Method]`)
+
+### Operator Details
+
+| | Description |
+| :--- | :--- |
+| **Operator Count** | `[1]` |
+| **Operator Skill Level** | `[ ] Expert (e.g., Surgeon, Sonographer)` <br> `[x] Intermediate (e.g., Trained Researcher)` <br> `[ ] Novice (e.g., ML Researcher with minimal experience)` <br> `[ ] N/A` |
+| **Collection Period** | From `[2025-12-26]` to `[2026-01-07]` |
+
+### Recovery Demonstrations
+
+*Does this dataset include examples of recovering from failure?*
+
+- [ ] **Yes**
+- [x] **No**
+
+<!-- **If yes, please briefly describe the recovery process:**
+
+*Example: For 250 demonstrations, demonstrations are initialized from a failed needle grasp position, the operator re-orients the robotic grippers and attempts to grasp the needle again from a different angle.* -->
+
+---
+
+## 💡 Diversity Dimensions
+
+*Check all dimensions that were intentionally varied during data collection.*
+
+- [ ] **Camera Position / Angle**
+- [ ] **Lighting Conditions**
+- [x] **Target Object** (e.g., different phantom models, suture types)
+- [x] **Spatial Layout** (e.g., placing the target suture needle in various locations)
+- [ ] **Robot Embodiment** (if multiple robots were used)
+- [ ] **Task Execution** (e.g., different techniques for the same task)
+- [ ] **Background / Scene**
+- [ ] **Other** (Please specify: `[Your Dimension]`)
+
+*If you checked any of the above please briefly elaborate below.*
+
+After each demonstration, we adjust the initial position of at least one of the robotic arm, ultrasonic probe, and target object (phantom).
+
+---
+
+## 🛠️ Equipment & Setup
+
+### Robotic Platform(s)
+
+*List the primary robot(s) used.*
+
+- **Robot 1:** `[UR5e]`
+- **Robot 2:** `[UR5e]`
+
+### Sensors & Cameras
+
+*List the sensors and cameras used. Specify model names where possible. (Add and remove rows as needed)*
+
+| Type | Model/Details |
+| :--- | :--- |
+| **Room/3rd Person Camera** | `[Intel RealSense D435i @ 30fps]` |
+| **Medical Imager** | `[Siemens ACUSON Juniper Ultrasound System with 12L3 Transducer, B-Mode]` |
+<!-- | **Other** | `[Specify]` | -->
+
+---
+
+## 🎯 Action & State Space Representation
+
+*Describe how actions and robot states are represented in your dataset. This is crucial for understanding data compatibility and enabling effective policy learning.*
+
+### Action Space Representation
+
+**Primary Action Representation:**
+- [x] **Absolute Cartesian** (position/orientation relative to robot base)
+- [ ] **Relative Cartesian** (delta position/orientation from current pose)
+- [ ] **Joint Space** (direct joint angle commands)
+- [ ] **Other** (Please specify: `[Your Representation]`)
+
+**Orientation Representation:**
+- [x] **Quaternions** (x, y, z, w)
+- [ ] **Euler Angles** (roll, pitch, yaw)
+- [ ] **Axis-Angle** (rotation vector)
+- [ ] **Rotation Matrix** (3x3 matrix)
+- [ ] **Other** (Please specify: `[Your Representation]`)
+
+**Reference Frame:**
+- [x] **Robot Base Frame**
+- [ ] **Tool/End-Effector Frame**
+- [ ] **World/Global Frame**
+- [ ] **Camera Frame**
+- [ ] **Other** (Please specify: `[Your Frame]`)
+
+<!-- **Action Dimensions:**
+*List the action space dimensions and their meanings.* -->
+
+<!-- **Example:** -->
+```
+action: ["left_x", "left_y", "left_z", "left_qx", "left_qy", "left_qz", "left_qw", "right_x", "right_y", "right_z", "right_qx", "right_qy", "right_qz", "right_qw"]
+- left_x, left_y, left_z, right_x, right_y, right_z: Absolute position in left and right robot base frame (meters)
+- left_qx, left_qy, left_qz, left_qw, right_qx, "right_qy, right_qz, right_qw: Absolute orientation as quaternion
+action.left: [x, y, z, qx, qy, qz, qw]
+- x, y, z: Absolute position in left robot base frame (meters)
+- qx, qy, qz, qw: Absolute orientation as quaternion
+action.right: [x, y, z, qx, qy, qz, qw]
+- x, y, z: Absolute position in right robot base frame (meters)
+- qx, qy, qz, qw: Absolute orientation as quaternion
+```
+<!-- - gripper: Gripper opening angle (radians) -->
+
+### State Space Representation
+
+**State Information Included:**
+- [x] **Joint Positions** (all articulated joints)
+- [ ] **Joint Velocities**
+- [ ] **End-Effector Pose** (Cartesian position/orientation)
+- [ ] **Force/Torque Readings**
+- [ ] **Gripper State** (position, force, etc.)
+- [ ] **Other** (Please specify: `[Your State Info]`)
+
+<!-- **State Dimensions:**
+*List the state space dimensions and their meanings.* -->
+
+**Example:**
+```
+observation.state: [j1, j2, j3, j4, j5, j6, j7, j8, j9, j10, j11, j12]
+- 'L_Base', 'L_Shoulder', 'L_Elbow', 'L_Wrist1', 'L_Wrist2' 'L_Wrist3' 'R_Base', 'R_Shoulder', 'R_Elbow', 'R_Wrist1', 'R_Wrist2', 'R_Wrist3' : Absolute joint positions for two 6-DOF arms (radians)
+observation.state.left: [j1, j2, j3, j4, j5, j6]
+- 'L_Base', 'L_Shoulder', 'L_Elbow', 'L_Wrist1', 'L_Wrist2' 'L_Wrist3' : Absolute joint positions for 6-DOF arm (radians)
+observation.state.right: [j1, j2, j3, j4, j5, j6]
+- 'R_Base', 'R_Shoulder', 'R_Elbow', 'R_Wrist1', 'R_Wrist2', 'R_Wrist3' : Absolute joint positions for 6-DOF arm (radians)
+```
+
+### 📋 Recommended Additional Representations
+**Step Information Included:**
+- [x] **Step Labels**
+
+**Example:**
+```
+observation.step_label: ["label"]
+- label: step labels represent the robot motion state, for each trajectory includes 3 labels and 5 motion sequences ('hold - alignment - hold - insertion - hold').
+```
+<!-- ### 📋 Recommended Additional Representations
+
+*Even if not your primary action/state representation, we strongly encourage including these standardized formats for maximum compatibility:*
+
+**Recommended Action Fields:**
+- **`action.cartesian_absolute`**: Absolute Cartesian pose with absolute quaternions
+  ```
+  [x, y, z, qx, qy, qz, qw, gripper_angle]
+  ```
+
+**Recommended State Fields:**
+- **`observation.state.joint_positions`**: Absolute positions for all articulated joints
+  ```
+  [joint_1, joint_2, ..., joint_n]
+  ``` -->
+
+
+---
+
+## ⏱️ Data Synchronization Approach
+<!-- 
+*Describe how you achieved proper data synchronization across different sensors, cameras, and robotic systems during data collection. This is crucial for ensuring temporal alignment of all modalities in your dataset.* -->
+
+*We collect joint kinematics from our UR5e, RGB-D frames from Intel RealSense D435 cameras and US images from Siemens Acuson Juniper with 12L3 transducer, all running in ROS 1 Noetic on the same workstation clocked with ROS Time. Both drivers stamp their outgoing messages’ header.stamp fields with the shared system clock, and we record /joint_states, /frame_grabber/us_img, /camera_top/color/image_raw, /right_tcp_pos, and /left_tcp_pos in a single rosbag session. During export to LeRobot, each data point’s ROS header.stamp is written verbatim into the timestamp attribute. Offline checks show inter-sensor skew stays below ±100 ms across a 2-minute capture.*
+
+---
+
+## 👥 Attribution & Contact
+
+*Please provide attribution for the dataset creators and a point of contact.*
+
+| | |
+| :--- | :--- |
+| **Dataset Lead** | `[Zhongyu Chen, Yunxi Tang, Tianqi Yang, Xiangyu Chu]` |
+| **Institution** | `[Bioinspired Robotics and Medical Technology (BMT) Lab, Department of Mechanical and Automation Engineering (MAE), The Chinese University of Hong Kong (CUHK) ]` |
+| **Contact Email** | `[zhongyuchen001@cuhk.edu.hk, 1155245457@link.cuhk.edu.hk, tianqiyang@cuhk.edu.hk, xychu@mae.cuhk.edu.hk]` |
+<!-- | **Citation (BibTeX)** | <pre><code>@misc{[your_dataset_name_2025],<br>  author = {[Your Name(s)]},<br>  title = {[Your Dataset Title]},<br>  year = {2025},<br>  publisher = {Open-H-Embodiment},<br>  note = {https://hrpp.research.virginia.edu/teams/irb-sbs/researcher-guide-irb-sbs/identifiers}<br>}</code></pre> | -->

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