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  1. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/data/chunk-000/episode_000035.parquet +3 -0
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  41. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/data/chunk-000/episode_000600.parquet +3 -0
  42. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/data/chunk-000/episode_000604.parquet +3 -0
  43. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/data/chunk-000/episode_000633.parquet +3 -0
  44. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/data/chunk-000/episode_000644.parquet +3 -0
  45. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/data/chunk-000/episode_000652.parquet +3 -0
  46. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/meta/README.md +234 -0
  47. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/meta/episodes.jsonl +665 -0
  48. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/meta/episodes_stats.jsonl +0 -0
  49. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/meta/info.json +347 -0
  50. Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/meta/tasks.jsonl +1 -0
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1
+ <!--
2
+ Open-H Embodiment Dataset README Template (v1.0)
3
+ Please fill out this template and include it in the ./metadata directory of your LeRobot dataset.
4
+ This file helps others understand the context and details of your contribution.
5
+ -->
6
+
7
+ # [Dataset Name] - README
8
+
9
+ ---
10
+
11
+ ## 📋 At a Glance
12
+
13
+ Static dual-arm in-plane needle insertion with interferences
14
+
15
+ ---
16
+
17
+ ## 📖 Dataset Overview
18
+
19
+ *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.*
20
+ <!-- It includes successful trials, failures, and recovery attempts to provide a robust dataset for training imitation learning policies.* -->
21
+
22
+ | | |
23
+ | :--- | :--- |
24
+ | **Total Trajectories** | `[2000]` |
25
+ | **Total Hours** | `[25]` |
26
+ | **Data Type** | `[ ] Clinical` `[x] Ex-Vivo` `[ ] Table-Top Phantom` `[ ] Digital Simulation` `[ ] Physical Simulation` `[ ] Other (If checked, update "Other")` |
27
+ | **License** | CC BY 4.0 |
28
+ | **Version** | `[1.0]` |
29
+
30
+ ---
31
+
32
+ ## 🎯 Tasks & Domain
33
+
34
+ ### Domain
35
+
36
+ *Select the primary domain for this dataset.*
37
+
38
+ - [ ] **Surgical Robotics**
39
+ - [x] **Ultrasound Robotics**
40
+ - [ ] **Other Healthcare Robotics** (Please specify: `[Your Domain]`)
41
+
42
+ ### Demonstrated Skills
43
+
44
+ *Autonomous dual-arm robotic needle insertion.*
45
+
46
+ <!-- ***Example:***
47
+ - Needle-insertion -->
48
+
49
+ ---
50
+
51
+ ## 🔬 Data Collection Details
52
+
53
+ ### Collection Method
54
+
55
+ *How was the data collected?*
56
+
57
+ - [ ] **Human Teleoperation**
58
+ - [x] **Programmatic/State-Machine**
59
+ - [ ] **AI Policy / Autonomous**
60
+ - [ ] **Other** (Please specify: `[Your Method]`)
61
+
62
+ ### Operator Details
63
+
64
+ | | Description |
65
+ | :--- | :--- |
66
+ | **Operator Count** | `[1]` |
67
+ | **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` |
68
+ | **Collection Period** | From `[2025-12-26]` to `[2026-01-07]` |
69
+
70
+ ### Recovery Demonstrations
71
+
72
+ *Does this dataset include examples of recovering from failure?*
73
+
74
+ - [ ] **Yes**
75
+ - [x] **No**
76
+
77
+ <!-- **If yes, please briefly describe the recovery process:**
78
+
79
+ *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.* -->
80
+
81
+ ---
82
+
83
+ ## 💡 Diversity Dimensions
84
+
85
+ *Check all dimensions that were intentionally varied during data collection.*
86
+
87
+ - [ ] **Camera Position / Angle**
88
+ - [ ] **Lighting Conditions**
89
+ - [x] **Target Object** (e.g., different phantom models, suture types)
90
+ - [x] **Spatial Layout** (e.g., placing the target suture needle in various locations)
91
+ - [ ] **Robot Embodiment** (if multiple robots were used)
92
+ - [ ] **Task Execution** (e.g., different techniques for the same task)
93
+ - [ ] **Background / Scene**
94
+ - [ ] **Other** (Please specify: `[Your Dimension]`)
95
+
96
+ *If you checked any of the above please briefly elaborate below.*
97
+
98
+ After each demonstration, we adjust the initial position of at least one of the robotic arm, ultrasonic probe, and target object (phantom).
99
+
100
+ ---
101
+
102
+ ## 🛠️ Equipment & Setup
103
+
104
+ ### Robotic Platform(s)
105
+
106
+ *List the primary robot(s) used.*
107
+
108
+ - **Robot 1:** `[UR5e]`
109
+ - **Robot 2:** `[UR5e]`
110
+
111
+ ### Sensors & Cameras
112
+
113
+ *List the sensors and cameras used. Specify model names where possible. (Add and remove rows as needed)*
114
+
115
+ | Type | Model/Details |
116
+ | :--- | :--- |
117
+ | **Room/3rd Person Camera** | `[Intel RealSense D435i @ 30fps]` |
118
+ | **Medical Imager** | `[Siemens ACUSON Juniper Ultrasound System with 12L3 Transducer, B-Mode]` |
119
+ <!-- | **Other** | `[Specify]` | -->
120
+
121
+ ---
122
+
123
+ ## 🎯 Action & State Space Representation
124
+
125
+ *Describe how actions and robot states are represented in your dataset. This is crucial for understanding data compatibility and enabling effective policy learning.*
126
+
127
+ ### Action Space Representation
128
+
129
+ **Primary Action Representation:**
130
+ - [x] **Absolute Cartesian** (position/orientation relative to robot base)
131
+ - [ ] **Relative Cartesian** (delta position/orientation from current pose)
132
+ - [ ] **Joint Space** (direct joint angle commands)
133
+ - [ ] **Other** (Please specify: `[Your Representation]`)
134
+
135
+ **Orientation Representation:**
136
+ - [x] **Quaternions** (x, y, z, w)
137
+ - [ ] **Euler Angles** (roll, pitch, yaw)
138
+ - [ ] **Axis-Angle** (rotation vector)
139
+ - [ ] **Rotation Matrix** (3x3 matrix)
140
+ - [ ] **Other** (Please specify: `[Your Representation]`)
141
+
142
+ **Reference Frame:**
143
+ - [x] **Robot Base Frame**
144
+ - [ ] **Tool/End-Effector Frame**
145
+ - [ ] **World/Global Frame**
146
+ - [ ] **Camera Frame**
147
+ - [ ] **Other** (Please specify: `[Your Frame]`)
148
+
149
+ <!-- **Action Dimensions:**
150
+ *List the action space dimensions and their meanings.* -->
151
+
152
+ <!-- **Example:** -->
153
+ ```
154
+ 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"]
155
+ - left_x, left_y, left_z, right_x, right_y, right_z: Absolute position in left and right robot base frame (meters)
156
+ - left_qx, left_qy, left_qz, left_qw, right_qx, "right_qy, right_qz, right_qw: Absolute orientation as quaternion
157
+ action.left: [x, y, z, qx, qy, qz, qw]
158
+ - x, y, z: Absolute position in left robot base frame (meters)
159
+ - qx, qy, qz, qw: Absolute orientation as quaternion
160
+ action.right: [x, y, z, qx, qy, qz, qw]
161
+ - x, y, z: Absolute position in right robot base frame (meters)
162
+ - qx, qy, qz, qw: Absolute orientation as quaternion
163
+ ```
164
+ <!-- - gripper: Gripper opening angle (radians) -->
165
+
166
+ ### State Space Representation
167
+
168
+ **State Information Included:**
169
+ - [x] **Joint Positions** (all articulated joints)
170
+ - [ ] **Joint Velocities**
171
+ - [ ] **End-Effector Pose** (Cartesian position/orientation)
172
+ - [ ] **Force/Torque Readings**
173
+ - [ ] **Gripper State** (position, force, etc.)
174
+ - [ ] **Other** (Please specify: `[Your State Info]`)
175
+
176
+ <!-- **State Dimensions:**
177
+ *List the state space dimensions and their meanings.* -->
178
+
179
+ **Example:**
180
+ ```
181
+ observation.state: [j1, j2, j3, j4, j5, j6, j7, j8, j9, j10, j11, j12]
182
+ - '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)
183
+ observation.state.left: [j1, j2, j3, j4, j5, j6]
184
+ - 'L_Base', 'L_Shoulder', 'L_Elbow', 'L_Wrist1', 'L_Wrist2' 'L_Wrist3' : Absolute joint positions for 6-DOF arm (radians)
185
+ observation.state.right: [j1, j2, j3, j4, j5, j6]
186
+ - 'R_Base', 'R_Shoulder', 'R_Elbow', 'R_Wrist1', 'R_Wrist2', 'R_Wrist3' : Absolute joint positions for 6-DOF arm (radians)
187
+ ```
188
+
189
+ ### 📋 Recommended Additional Representations
190
+ **Step Information Included:**
191
+ - [x] **Step Labels**
192
+
193
+ **Example:**
194
+ ```
195
+ observation.step_label: ["label"]
196
+ - label: step labels represent the robot motion state, for each trajectory includes 3 labels and 5 motion sequences ('hold - alignment - hold - insertion - hold').
197
+ ```
198
+ <!-- ### 📋 Recommended Additional Representations
199
+
200
+ *Even if not your primary action/state representation, we strongly encourage including these standardized formats for maximum compatibility:*
201
+
202
+ **Recommended Action Fields:**
203
+ - **`action.cartesian_absolute`**: Absolute Cartesian pose with absolute quaternions
204
+ ```
205
+ [x, y, z, qx, qy, qz, qw, gripper_angle]
206
+ ```
207
+
208
+ **Recommended State Fields:**
209
+ - **`observation.state.joint_positions`**: Absolute positions for all articulated joints
210
+ ```
211
+ [joint_1, joint_2, ..., joint_n]
212
+ ``` -->
213
+
214
+
215
+ ---
216
+
217
+ ## ⏱️ Data Synchronization Approach
218
+ <!--
219
+ *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.* -->
220
+
221
+ *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.*
222
+
223
+ ---
224
+
225
+ ## 👥 Attribution & Contact
226
+
227
+ *Please provide attribution for the dataset creators and a point of contact.*
228
+
229
+ | | |
230
+ | :--- | :--- |
231
+ | **Dataset Lead** | `[Zhongyu Chen, Yunxi Tang, Tianqi Yang, Xiangyu Chu]` |
232
+ | **Institution** | `[Bioinspired Robotics and Medical Technology (BMT) Lab, Department of Mechanical and Automation Engineering (MAE), The Chinese University of Hong Kong (CUHK) ]` |
233
+ | **Contact Email** | `[zhongyuchen001@cuhk.edu.hk, 1155245457@link.cuhk.edu.hk, tianqiyang@cuhk.edu.hk, xychu@mae.cuhk.edu.hk]` |
234
+ <!-- | **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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Ultrasound/cuhk/xiangyu_chu/bmt_insertion_vessel_dataset/bmt_insertion_vessel_dataset/meta/tasks.jsonl ADDED
@@ -0,0 +1 @@
 
 
1
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