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Browse files- Ultrasound/balgrist/sonogym_open_h_us_guidance_l1/meta/README.md +231 -0
- Ultrasound/balgrist/sonogym_open_h_us_guidance_l1/meta/episodes.jsonl +1024 -0
- Ultrasound/balgrist/sonogym_open_h_us_guidance_l1/meta/episodes_stats.jsonl +0 -0
- Ultrasound/balgrist/sonogym_open_h_us_guidance_l1/meta/info.json +151 -0
- Ultrasound/balgrist/sonogym_open_h_us_guidance_l1/meta/tasks.jsonl +1 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/README.md +226 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/episodes.jsonl +60 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/episodes_stats.jsonl +0 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/info.json +132 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/tasks.jsonl +2 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/README.md +228 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/episodes.jsonl +12 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/episodes_stats.jsonl +12 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/info.json +163 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/tasks.jsonl +2 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full_3_synthetic_robot_2/meta/README.md +228 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full_3_synthetic_robot_2/meta/episodes.jsonl +18 -0
- Ultrasound/balgrist/ultrabones_lerobot_dataset_full_3_synthetic_robot_2/meta/info.json +163 -0
Ultrasound/balgrist/sonogym_open_h_us_guidance_l1/meta/README.md
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| 1 |
+
<!--
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| 2 |
+
Open-H Embodiment Dataset README Template (v1.0)
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| 3 |
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Please fill out this template and include it in the ./metadata directory of your LeRobot dataset.
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| 4 |
+
This file helps others understand the context and details of your contribution.
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| 5 |
+
-->
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| 6 |
+
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| 7 |
+
# SonoGym Probe Manipulation Lerobot Dataset_1 - README
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| 8 |
+
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
## 📋 At a Glance
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| 12 |
+
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| 13 |
+
*Synthetic ultrasound probe manipulation to see L1 vertebra.*
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| 14 |
+
|
| 15 |
+
---
|
| 16 |
+
|
| 17 |
+
## 📖 Dataset Overview
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| 18 |
+
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| 19 |
+
*This dataset contains 1024 trajectories of expert policies to move the probe to be above the target vertebra.*
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| 20 |
+
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| 21 |
+
| | |
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| 22 |
+
| :--- | :--- |
|
| 23 |
+
| **Total Trajectories** | `[1024]` |
|
| 24 |
+
| **Total Hours** | `[]` |
|
| 25 |
+
| **Data Type** | `[ ] Clinical` `[] Ex-Vivo` `[ ] Table-Top Phantom` `[x] Digital Simulation` `[ ] Physical Simulation` `[ ] Other (If checked, update "Other")` |
|
| 26 |
+
| **License** | CC BY 4.0 |
|
| 27 |
+
| **Version** | `[e.g., 1.0]` |
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| 28 |
+
|
| 29 |
+
---
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| 30 |
+
|
| 31 |
+
## 🎯 Tasks & Domain
|
| 32 |
+
|
| 33 |
+
### Domain
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| 34 |
+
|
| 35 |
+
*Select the primary domain for this dataset.*
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| 36 |
+
|
| 37 |
+
- [ ] **Surgical Robotics**
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| 38 |
+
- [x] **Ultrasound Robotics**
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| 39 |
+
- [ ] **Other Healthcare Robotics** (Please specify: `[Your Domain]`)
|
| 40 |
+
|
| 41 |
+
### Demonstrated Skills
|
| 42 |
+
|
| 43 |
+
- Navigate ultrasound prove to find a specific anatomy
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| 44 |
+
|
| 45 |
+
---
|
| 46 |
+
|
| 47 |
+
## 🔬 Data Collection Details
|
| 48 |
+
|
| 49 |
+
### Collection Method
|
| 50 |
+
|
| 51 |
+
*How was the data collected?*
|
| 52 |
+
|
| 53 |
+
- [ ] **Human Teleoperation**
|
| 54 |
+
- [x] **Programmatic/State-Machine**
|
| 55 |
+
- [ ] **AI Policy / Autonomous**
|
| 56 |
+
- [ ] **Other** (Please specify: `[]`)
|
| 57 |
+
|
| 58 |
+
### Operator Details
|
| 59 |
+
|
| 60 |
+
| | Description |
|
| 61 |
+
| :--- | :--- |
|
| 62 |
+
| **Operator Count** | `[1]` |
|
| 63 |
+
| **Operator Skill Level** | `[ ] Expert (e.g., Surgeon, Sonographer)` <br> `[ ] Intermediate (e.g., Trained Researcher)` <br> `[ ] Novice (e.g., ML Researcher with minimal experience)` <br> `[x] N/A` |
|
| 64 |
+
| **Collection Period** | From `[2025-03]` to `[2026-01]` |
|
| 65 |
+
|
| 66 |
+
### Recovery Demonstrations
|
| 67 |
+
|
| 68 |
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*Does this dataset include examples of recovering from failure?*
|
| 69 |
+
|
| 70 |
+
- [ ] **Yes**
|
| 71 |
+
- [x] **No**
|
| 72 |
+
|
| 73 |
+
**If yes, please briefly describe the recovery process:**
|
| 74 |
+
|
| 75 |
+
---
|
| 76 |
+
|
| 77 |
+
## 💡 Diversity Dimensions
|
| 78 |
+
|
| 79 |
+
*Check all dimensions that were intentionally varied during data collection.*
|
| 80 |
+
|
| 81 |
+
- [x] **Camera Position / Angle**
|
| 82 |
+
- [ ] **Lighting Conditions**
|
| 83 |
+
- [x] **Target Object** (e.g., different phantom models, suture types)
|
| 84 |
+
- [x] **Spatial Layout** (e.g., placing the target suture needle in various locations)
|
| 85 |
+
- [ ] **Robot Embodiment** (if multiple robots were used)
|
| 86 |
+
- [ ] **Task Execution** (e.g., different techniques for the same task)
|
| 87 |
+
- [ ] **Background / Scene**
|
| 88 |
+
- [ ] **Other** (Please specify: `[Your Dimension]`)
|
| 89 |
+
|
| 90 |
+
*If you checked any of the above please briefly elaborate below.*
|
| 91 |
+
|
| 92 |
+
We start the ultrasound scan from various different initial positions. We scan multiple bone structures including vertebra L1-L5.
|
| 93 |
+
|
| 94 |
+
---
|
| 95 |
+
|
| 96 |
+
## 🛠️ Equipment & Setup
|
| 97 |
+
|
| 98 |
+
### Robotic Platform(s)
|
| 99 |
+
|
| 100 |
+
*List the primary robot(s) used.*
|
| 101 |
+
|
| 102 |
+
*Kuka med14*
|
| 103 |
+
|
| 104 |
+
|
| 105 |
+
### Sensors & Cameras
|
| 106 |
+
|
| 107 |
+
*List the sensors and cameras used. Specify model names where possible. (Add and remove rows as needed)*
|
| 108 |
+
|
| 109 |
+
| Type | Model/Details |
|
| 110 |
+
| :--- | :--- |
|
| 111 |
+
| **Primary Camera** | `[None]` |
|
| 112 |
+
| **Room/3rd Person Camera** | `[None]` |
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| 113 |
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| **Force/Torque Sensor** | `[None]` |
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| 114 |
+
| **Medical Imager** | `[e.g., Synthetic ultrasound by GAN, B-Mode]` |
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| 115 |
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| **Other** | `[Specify]` |
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| 116 |
+
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| 117 |
+
---
|
| 118 |
+
|
| 119 |
+
## 🎯 Action & State Space Representation
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| 120 |
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| 121 |
+
*Describe how actions and robot states are represented in your dataset. This is crucial for understanding data compatibility and enabling effective policy learning.*
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| 122 |
+
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| 123 |
+
### Action Space Representation
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| 124 |
+
|
| 125 |
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**Primary Action Representation:**
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| 126 |
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- [ ] **Absolute Cartesian** (position/orientation relative to robot base)
|
| 127 |
+
- [x] **Relative Cartesian** (delta position/orientation from current pose)
|
| 128 |
+
- [ ] **Joint Space** (direct joint angle commands)
|
| 129 |
+
- [ ] **Other** (Please specify: `[Your Representation]`)
|
| 130 |
+
|
| 131 |
+
**Orientation Representation:**
|
| 132 |
+
- [x] **Quaternions** (x, y, z, w)
|
| 133 |
+
- [ ] **Euler Angles** (roll, pitch, yaw)
|
| 134 |
+
- [ ] **Axis-Angle** (rotation vector)
|
| 135 |
+
- [ ] **Rotation Matrix** (3x3 matrix)
|
| 136 |
+
- [ ] **Other** (Please specify: `None`)
|
| 137 |
+
|
| 138 |
+
**Reference Frame:**
|
| 139 |
+
- [ ] **Robot Base Frame**
|
| 140 |
+
- [x] **Tool/End-Effector Frame**
|
| 141 |
+
- [ ] **World/Global Frame**
|
| 142 |
+
- [ ] **Camera Frame**
|
| 143 |
+
- [ ] **Other** (Please specify: `[Your Frame]`)
|
| 144 |
+
|
| 145 |
+
**Action Dimensions:**
|
| 146 |
+
*List the action space dimensions and their meanings.*
|
| 147 |
+
```
|
| 148 |
+
action: [x, y, z, qw, qx, qy, qz]
|
| 149 |
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- x, y, z: relative position in ultrasound image frame (meters) to next pose.
|
| 150 |
+
- qw, qx, qy, qz: quaternion rotation to the next frame
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| 151 |
+
- Note that this 3D motion is along the surface of the training patient model. This may not align with the testing case.
|
| 152 |
+
```
|
| 153 |
+
|
| 154 |
+
|
| 155 |
+
**Example:**
|
| 156 |
+
```
|
| 157 |
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action: [x, y, z, qx, qy, qz, qw, gripper]
|
| 158 |
+
- x, y, z: Absolute position in robot base frame (meters)
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| 159 |
+
- qx, qy, qz, qw: Absolute orientation as quaternion
|
| 160 |
+
- gripper: Gripper opening angle (radians)
|
| 161 |
+
```
|
| 162 |
+
|
| 163 |
+
### State Space Representation
|
| 164 |
+
|
| 165 |
+
**State Information Included:**
|
| 166 |
+
- [x] **Joint Positions** (all articulated joints)
|
| 167 |
+
- [ ] **Joint Velocities**
|
| 168 |
+
- [x] **End-Effector Pose** (Cartesian position/orientation)
|
| 169 |
+
- [ ] **Force/Torque Readings**
|
| 170 |
+
- [ ] **Gripper State** (position, force, etc.)
|
| 171 |
+
- [ ] **Other** (Please specify: `[Your State Info]`)
|
| 172 |
+
|
| 173 |
+
**State Dimensions:**
|
| 174 |
+
*List the state space dimensions and their meanings.*
|
| 175 |
+
|
| 176 |
+
```
|
| 177 |
+
observation.state.ee_state: [x, y, z, qw, qx, qy, qz]
|
| 178 |
+
- x, y, z: Absolute position in base frame (meters)
|
| 179 |
+
- qw, qx, qy, qz: quaternion in base frame
|
| 180 |
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observation.state.joint_state: [j1, j2, j3, j4, j5, j6, j7]
|
| 181 |
+
- Absolute joint positions for 7-DOF arm (radians)
|
| 182 |
+
```
|
| 183 |
+
|
| 184 |
+
|
| 185 |
+
**Example:**
|
| 186 |
+
```
|
| 187 |
+
observation.state: [j1, j2, j3, j4, j5, j6, j7, gripper_pos]
|
| 188 |
+
- j1-j7: Absolute joint positions for 7-DOF arm (radians)
|
| 189 |
+
- gripper_pos: Current gripper opening (meters)
|
| 190 |
+
```
|
| 191 |
+
|
| 192 |
+
### 📋 Recommended Additional Representations
|
| 193 |
+
|
| 194 |
+
*Even if not your primary action/state representation, we strongly encourage including these standardized formats for maximum compatibility:*
|
| 195 |
+
|
| 196 |
+
**Recommended Action Fields:**
|
| 197 |
+
- **`action.cartesian_absolute`**: Absolute Cartesian pose with absolute quaternions
|
| 198 |
+
```
|
| 199 |
+
[x, y, z, qx, qy, qz, qw, gripper_angle]
|
| 200 |
+
```
|
| 201 |
+
|
| 202 |
+
**Recommended State Fields:**
|
| 203 |
+
- **`observation.state.joint_positions`**: Absolute positions for all articulated joints
|
| 204 |
+
```
|
| 205 |
+
[joint_1, joint_2, ..., joint_n]
|
| 206 |
+
```
|
| 207 |
+
|
| 208 |
+
|
| 209 |
+
---
|
| 210 |
+
|
| 211 |
+
## ⏱️ Data Synchronization Approach
|
| 212 |
+
|
| 213 |
+
|
| 214 |
+
*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.*
|
| 215 |
+
|
| 216 |
+
*The synchronization is ensured automatically through simulation*.
|
| 217 |
+
|
| 218 |
+
**Example:** *We collect joint kinematics from our Franka Research 3 and RGB-D frames from Intel RealSense D435 cameras, all running in ROS 2 Galactic 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, /camera/*/image_raw, and /camera/*/camera_info in a single rosbag2 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 ±2 ms across a 2-minute capture.*
|
| 219 |
+
|
| 220 |
+
---
|
| 221 |
+
|
| 222 |
+
## 👥 Attribution & Contact
|
| 223 |
+
|
| 224 |
+
*Please provide attribution for the dataset creators and a point of contact.*
|
| 225 |
+
|
| 226 |
+
| | |
|
| 227 |
+
| :--- | :--- |
|
| 228 |
+
| **Dataset Lead** | `[Yunke Ao, Luohong Wu]` |
|
| 229 |
+
| **Institution** | `[Balgrist University Hospital]` |
|
| 230 |
+
| **Contact Email** | `[yunke.ao@balgrist.ch, luohong.wu@balgrist.ch, ...]` |
|
| 231 |
+
| **Citation (BibTeX)** | <pre><code>@misc{[Ultrabones100k_lerobot_2026],<br> author = {[Yunke Ao, Luohong Wu, Philipp Fuernstahl]},<br> title = {[Ultrabones100k Lerobot Dataset]},<br> year = {2026},<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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| 1008 |
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{"episode_index": 1007, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1009 |
+
{"episode_index": 1008, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1010 |
+
{"episode_index": 1009, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1011 |
+
{"episode_index": 1010, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1012 |
+
{"episode_index": 1011, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1013 |
+
{"episode_index": 1012, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1014 |
+
{"episode_index": 1013, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1015 |
+
{"episode_index": 1014, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1016 |
+
{"episode_index": 1015, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1017 |
+
{"episode_index": 1016, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1018 |
+
{"episode_index": 1017, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1019 |
+
{"episode_index": 1018, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1020 |
+
{"episode_index": 1019, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1021 |
+
{"episode_index": 1020, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1022 |
+
{"episode_index": 1021, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1023 |
+
{"episode_index": 1022, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
| 1024 |
+
{"episode_index": 1023, "tasks": ["Move the ultrasound probe to vertebraL4"], "length": 179}
|
Ultrasound/balgrist/sonogym_open_h_us_guidance_l1/meta/episodes_stats.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
Ultrasound/balgrist/sonogym_open_h_us_guidance_l1/meta/info.json
ADDED
|
@@ -0,0 +1,151 @@
|
|
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|
| 1 |
+
{
|
| 2 |
+
"codebase_version": "v2.1",
|
| 3 |
+
"robot_type": "kuka-med14",
|
| 4 |
+
"total_episodes": 1024,
|
| 5 |
+
"total_frames": 183296,
|
| 6 |
+
"total_tasks": 1,
|
| 7 |
+
"total_videos": 1024,
|
| 8 |
+
"total_chunks": 2,
|
| 9 |
+
"chunks_size": 1000,
|
| 10 |
+
"fps": 120,
|
| 11 |
+
"splits": {
|
| 12 |
+
"train": "0:819",
|
| 13 |
+
"val": "819:921",
|
| 14 |
+
"test": "921:1024"
|
| 15 |
+
},
|
| 16 |
+
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
|
| 17 |
+
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
|
| 18 |
+
"features": {
|
| 19 |
+
"observation.images.ultrasound": {
|
| 20 |
+
"dtype": "video",
|
| 21 |
+
"shape": [
|
| 22 |
+
200,
|
| 23 |
+
150,
|
| 24 |
+
3
|
| 25 |
+
],
|
| 26 |
+
"names": [
|
| 27 |
+
"height",
|
| 28 |
+
"width",
|
| 29 |
+
"channel"
|
| 30 |
+
],
|
| 31 |
+
"info": {
|
| 32 |
+
"video.height": 200,
|
| 33 |
+
"video.width": 150,
|
| 34 |
+
"video.codec": "av1",
|
| 35 |
+
"video.pix_fmt": "yuv420p",
|
| 36 |
+
"video.is_depth_map": false,
|
| 37 |
+
"video.fps": 120,
|
| 38 |
+
"video.channels": 3,
|
| 39 |
+
"has_audio": false
|
| 40 |
+
}
|
| 41 |
+
},
|
| 42 |
+
"observation.state.ee_state": {
|
| 43 |
+
"dtype": "float32",
|
| 44 |
+
"shape": [
|
| 45 |
+
7
|
| 46 |
+
],
|
| 47 |
+
"names": [
|
| 48 |
+
"x",
|
| 49 |
+
"y",
|
| 50 |
+
"z",
|
| 51 |
+
"qw",
|
| 52 |
+
"qx",
|
| 53 |
+
"qy",
|
| 54 |
+
"qz"
|
| 55 |
+
]
|
| 56 |
+
},
|
| 57 |
+
"observation.state.joint_state": {
|
| 58 |
+
"dtype": "float32",
|
| 59 |
+
"shape": [
|
| 60 |
+
7
|
| 61 |
+
],
|
| 62 |
+
"names": [
|
| 63 |
+
"joint_1",
|
| 64 |
+
"joint_2",
|
| 65 |
+
"joint_3",
|
| 66 |
+
"joint_4",
|
| 67 |
+
"joint_5",
|
| 68 |
+
"joint_6",
|
| 69 |
+
"joint_7"
|
| 70 |
+
]
|
| 71 |
+
},
|
| 72 |
+
"action": {
|
| 73 |
+
"dtype": "float32",
|
| 74 |
+
"shape": [
|
| 75 |
+
7
|
| 76 |
+
],
|
| 77 |
+
"names": [
|
| 78 |
+
"x",
|
| 79 |
+
"y",
|
| 80 |
+
"z",
|
| 81 |
+
"qw",
|
| 82 |
+
"qx",
|
| 83 |
+
"qy",
|
| 84 |
+
"qz"
|
| 85 |
+
]
|
| 86 |
+
},
|
| 87 |
+
"instruction.text": {
|
| 88 |
+
"dtype": "string",
|
| 89 |
+
"shape": [
|
| 90 |
+
1
|
| 91 |
+
],
|
| 92 |
+
"description": "Natural language command for the robot"
|
| 93 |
+
},
|
| 94 |
+
"observation.meta.probe_type": {
|
| 95 |
+
"dtype": "string",
|
| 96 |
+
"shape": [
|
| 97 |
+
1
|
| 98 |
+
],
|
| 99 |
+
"names": [
|
| 100 |
+
"synthetic probe"
|
| 101 |
+
]
|
| 102 |
+
},
|
| 103 |
+
"observation.meta.probe_acquisition_param": {
|
| 104 |
+
"dtype": "float32",
|
| 105 |
+
"shape": [
|
| 106 |
+
4
|
| 107 |
+
],
|
| 108 |
+
"names": [
|
| 109 |
+
"center_frequency_mhz",
|
| 110 |
+
"num_elements",
|
| 111 |
+
"imaging_depth_cm",
|
| 112 |
+
"imaging_width_cm"
|
| 113 |
+
]
|
| 114 |
+
},
|
| 115 |
+
"timestamp": {
|
| 116 |
+
"dtype": "float32",
|
| 117 |
+
"shape": [
|
| 118 |
+
1
|
| 119 |
+
],
|
| 120 |
+
"names": null
|
| 121 |
+
},
|
| 122 |
+
"frame_index": {
|
| 123 |
+
"dtype": "int64",
|
| 124 |
+
"shape": [
|
| 125 |
+
1
|
| 126 |
+
],
|
| 127 |
+
"names": null
|
| 128 |
+
},
|
| 129 |
+
"episode_index": {
|
| 130 |
+
"dtype": "int64",
|
| 131 |
+
"shape": [
|
| 132 |
+
1
|
| 133 |
+
],
|
| 134 |
+
"names": null
|
| 135 |
+
},
|
| 136 |
+
"index": {
|
| 137 |
+
"dtype": "int64",
|
| 138 |
+
"shape": [
|
| 139 |
+
1
|
| 140 |
+
],
|
| 141 |
+
"names": null
|
| 142 |
+
},
|
| 143 |
+
"task_index": {
|
| 144 |
+
"dtype": "int64",
|
| 145 |
+
"shape": [
|
| 146 |
+
1
|
| 147 |
+
],
|
| 148 |
+
"names": null
|
| 149 |
+
}
|
| 150 |
+
}
|
| 151 |
+
}
|
Ultrasound/balgrist/sonogym_open_h_us_guidance_l1/meta/tasks.jsonl
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"task_index": 0, "task": "Move the ultrasound probe to vertebraL4"}
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/README.md
ADDED
|
@@ -0,0 +1,226 @@
|
|
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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 |
+
# Ultrabones100k Cadaveric Ultrasound Lerobot Dataset_1 - README
|
| 8 |
+
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
## 📋 At a Glance
|
| 12 |
+
|
| 13 |
+
*Freehand tracked ultrasound scan from an expert surgeon (first part ), with 14 cadavers and 5-12 different scans for each of them. Both Tibia and Fibula are scanned for different cadavers.*
|
| 14 |
+
|
| 15 |
+
---
|
| 16 |
+
|
| 17 |
+
## 📖 Dataset Overview
|
| 18 |
+
|
| 19 |
+
*This dataset contains 60 trajectories of expert surgeons using the ultrasound probe to scan bones. The state is the current pose. Observation is the ultrasound image. The action is extracted as the delta cartesion pose to 1 second later.*
|
| 20 |
+
|
| 21 |
+
| | |
|
| 22 |
+
| :--- | :--- |
|
| 23 |
+
| **Total Trajectories** | `[60]` |
|
| 24 |
+
| **Total Hours** | `[1.2]` |
|
| 25 |
+
| **Data Type** | `[ ] Clinical` `[x] Ex-Vivo` `[ ] Table-Top Phantom` `[ ] Digital Simulation` `[ ] Physical Simulation` `[ ] Other (If checked, update "Other")` |
|
| 26 |
+
| **License** | CC BY 4.0 |
|
| 27 |
+
| **Version** | `[e.g., 1.0]` |
|
| 28 |
+
|
| 29 |
+
---
|
| 30 |
+
|
| 31 |
+
## 🎯 Tasks & Domain
|
| 32 |
+
|
| 33 |
+
### Domain
|
| 34 |
+
|
| 35 |
+
*Select the primary domain for this dataset.*
|
| 36 |
+
|
| 37 |
+
- [ ] **Surgical Robotics**
|
| 38 |
+
- [x] **Ultrasound Robotics**
|
| 39 |
+
- [ ] **Other Healthcare Robotics** (Please specify: `[Your Domain]`)
|
| 40 |
+
|
| 41 |
+
### Demonstrated Skills
|
| 42 |
+
|
| 43 |
+
- Ultrasound scanning for orthopedics / reconstruction (fibula and tibia)
|
| 44 |
+
|
| 45 |
+
---
|
| 46 |
+
|
| 47 |
+
## 🔬 Data Collection Details
|
| 48 |
+
|
| 49 |
+
### Collection Method
|
| 50 |
+
|
| 51 |
+
*How was the data collected?*
|
| 52 |
+
|
| 53 |
+
- [ ] **Human Teleoperation**
|
| 54 |
+
- [ ] **Programmatic/State-Machine**
|
| 55 |
+
- [ ] **AI Policy / Autonomous**
|
| 56 |
+
- [x] **Other** (Please specify: `[Expert freehand]`)
|
| 57 |
+
|
| 58 |
+
### Operator Details
|
| 59 |
+
|
| 60 |
+
| | Description |
|
| 61 |
+
| :--- | :--- |
|
| 62 |
+
| **Operator Count** | `[1]` |
|
| 63 |
+
| **Operator Skill Level** | `[x] Expert (e.g., Surgeon, Sonographer)` <br> `[ ] Intermediate (e.g., Trained Researcher)` <br> `[ ] Novice (e.g., ML Researcher with minimal experience)` <br> `[ ] N/A` |
|
| 64 |
+
| **Collection Period** | From `[2024-01]` to `[2024-06]` |
|
| 65 |
+
|
| 66 |
+
### Recovery Demonstrations
|
| 67 |
+
|
| 68 |
+
*Does this dataset include examples of recovering from failure?*
|
| 69 |
+
|
| 70 |
+
- [ ] **Yes**
|
| 71 |
+
- [x] **No**
|
| 72 |
+
|
| 73 |
+
**If yes, please briefly describe the recovery process:**
|
| 74 |
+
|
| 75 |
+
---
|
| 76 |
+
|
| 77 |
+
## 💡 Diversity Dimensions
|
| 78 |
+
|
| 79 |
+
*Check all dimensions that were intentionally varied during data collection.*
|
| 80 |
+
|
| 81 |
+
- [x] **Camera Position / Angle**
|
| 82 |
+
- [ ] **Lighting Conditions**
|
| 83 |
+
- [x] **Target Object** (e.g., different phantom models, suture types)
|
| 84 |
+
- [x] **Spatial Layout** (e.g., placing the target suture needle in various locations)
|
| 85 |
+
- [ ] **Robot Embodiment** (if multiple robots were used)
|
| 86 |
+
- [x] **Task Execution** (e.g., different techniques for the same task)
|
| 87 |
+
- [ ] **Background / Scene**
|
| 88 |
+
- [ ] **Other** (Please specify: `[Your Dimension]`)
|
| 89 |
+
|
| 90 |
+
*If you checked any of the above please briefly elaborate below.*
|
| 91 |
+
|
| 92 |
+
We start the ultrasound scan from different initial positions. We scan multiple bone structures including fibla and tibia. We scan 14 different cadavers with 5-12 various recordings for each of them.
|
| 93 |
+
|
| 94 |
+
---
|
| 95 |
+
|
| 96 |
+
## 🛠️ Equipment & Setup
|
| 97 |
+
|
| 98 |
+
### Robotic Platform(s)
|
| 99 |
+
|
| 100 |
+
*List the primary robot(s) used.*
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
### Sensors & Cameras
|
| 104 |
+
|
| 105 |
+
*List the sensors and cameras used. Specify model names where possible. (Add and remove rows as needed)*
|
| 106 |
+
|
| 107 |
+
| Type | Model/Details |
|
| 108 |
+
| :--- | :--- |
|
| 109 |
+
| **Primary Camera** | `[None]` |
|
| 110 |
+
| **Room/3rd Person Camera** | `[None]` |
|
| 111 |
+
| **Force/Torque Sensor** | `[None]` |
|
| 112 |
+
| **Medical Imager** | `[e.g., SuperSonic Imagine SL18-5 Ultrasound, B-Mode]` |
|
| 113 |
+
| **Other** | `[Specify]` |
|
| 114 |
+
|
| 115 |
+
---
|
| 116 |
+
|
| 117 |
+
## 🎯 Action & State Space Representation
|
| 118 |
+
|
| 119 |
+
*Describe how actions and robot states are represented in your dataset. This is crucial for understanding data compatibility and enabling effective policy learning.*
|
| 120 |
+
|
| 121 |
+
### Action Space Representation
|
| 122 |
+
|
| 123 |
+
**Primary Action Representation:**
|
| 124 |
+
- [ ] **Absolute Cartesian** (position/orientation relative to robot base)
|
| 125 |
+
- [x] **Relative Cartesian** (delta position/orientation from current pose)
|
| 126 |
+
- [ ] **Joint Space** (direct joint angle commands)
|
| 127 |
+
- [ ] **Other** (Please specify: `[Your Representation]`)
|
| 128 |
+
|
| 129 |
+
**Orientation Representation:**
|
| 130 |
+
- [ ] **Quaternions** (x, y, z, w)
|
| 131 |
+
- [ ] **Euler Angles** (roll, pitch, yaw)
|
| 132 |
+
- [ ] **Axis-Angle** (rotation vector)
|
| 133 |
+
- [ ] **Rotation Matrix** (3x3 matrix)
|
| 134 |
+
- [x] **Other** (Please specify: `extrinsic xyz euler angle`)
|
| 135 |
+
|
| 136 |
+
**Reference Frame:**
|
| 137 |
+
- [ ] **Robot Base Frame**
|
| 138 |
+
- [x] **Tool/End-Effector Frame**
|
| 139 |
+
- [ ] **World/Global Frame**
|
| 140 |
+
- [ ] **Camera Frame**
|
| 141 |
+
- [ ] **Other** (Please specify: `[Your Frame]`)
|
| 142 |
+
|
| 143 |
+
**Action Dimensions:**
|
| 144 |
+
*List the action space dimensions and their meanings.*
|
| 145 |
+
```
|
| 146 |
+
action: [x, y, z, euler_x, euler_y, euler_z]
|
| 147 |
+
- x, y, z: relative position in ultrasound image frame (meters) to 1 sec later
|
| 148 |
+
- euler_x, euler_y, euler_z: extrinsic xyz euler angle in image frame to 1 sec later
|
| 149 |
+
```
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
**Example:**
|
| 153 |
+
```
|
| 154 |
+
action: [x, y, z, qx, qy, qz, qw, gripper]
|
| 155 |
+
- x, y, z: Absolute position in robot base frame (meters)
|
| 156 |
+
- qx, qy, qz, qw: Absolute orientation as quaternion
|
| 157 |
+
- gripper: Gripper opening angle (radians)
|
| 158 |
+
```
|
| 159 |
+
|
| 160 |
+
### State Space Representation
|
| 161 |
+
|
| 162 |
+
**State Information Included:**
|
| 163 |
+
- [ ] **Joint Positions** (all articulated joints)
|
| 164 |
+
- [ ] **Joint Velocities**
|
| 165 |
+
- [x] **End-Effector Pose** (Cartesian position/orientation)
|
| 166 |
+
- [ ] **Force/Torque Readings**
|
| 167 |
+
- [ ] **Gripper State** (position, force, etc.)
|
| 168 |
+
- [ ] **Other** (Please specify: `[Your State Info]`)
|
| 169 |
+
|
| 170 |
+
**State Dimensions:**
|
| 171 |
+
*List the state space dimensions and their meanings.*
|
| 172 |
+
|
| 173 |
+
```
|
| 174 |
+
observation.state: [x, y, z, euler_x, euler_y, euler_z]
|
| 175 |
+
- x, y, z: Absolute position in world frame (meters)
|
| 176 |
+
- euler_x, euler_y, euler_z: extrinsic xyz euler angle XYZ euler angle in world frame
|
| 177 |
+
```
|
| 178 |
+
|
| 179 |
+
|
| 180 |
+
**Example:**
|
| 181 |
+
```
|
| 182 |
+
observation.state: [j1, j2, j3, j4, j5, j6, j7, gripper_pos]
|
| 183 |
+
- j1-j7: Absolute joint positions for 7-DOF arm (radians)
|
| 184 |
+
- gripper_pos: Current gripper opening (meters)
|
| 185 |
+
```
|
| 186 |
+
|
| 187 |
+
### 📋 Recommended Additional Representations
|
| 188 |
+
|
| 189 |
+
*Even if not your primary action/state representation, we strongly encourage including these standardized formats for maximum compatibility:*
|
| 190 |
+
|
| 191 |
+
**Recommended Action Fields:**
|
| 192 |
+
- **`action.cartesian_absolute`**: Absolute Cartesian pose with absolute quaternions
|
| 193 |
+
```
|
| 194 |
+
[x, y, z, qx, qy, qz, qw, gripper_angle]
|
| 195 |
+
```
|
| 196 |
+
|
| 197 |
+
**Recommended State Fields:**
|
| 198 |
+
- **`observation.state.joint_positions`**: Absolute positions for all articulated joints
|
| 199 |
+
```
|
| 200 |
+
[joint_1, joint_2, ..., joint_n]
|
| 201 |
+
```
|
| 202 |
+
|
| 203 |
+
|
| 204 |
+
---
|
| 205 |
+
|
| 206 |
+
## ⏱️ Data Synchronization Approach
|
| 207 |
+
|
| 208 |
+
|
| 209 |
+
*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.*
|
| 210 |
+
|
| 211 |
+
*The synchronization is part of of calibration procedure, which is detailed in [Ultrabones100k](https://arxiv.org/abs/2502.03783)*.
|
| 212 |
+
|
| 213 |
+
**Example:** *We collect joint kinematics from our Franka Research 3 and RGB-D frames from Intel RealSense D435 cameras, all running in ROS 2 Galactic 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, /camera/*/image_raw, and /camera/*/camera_info in a single rosbag2 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 ±2 ms across a 2-minute capture.*
|
| 214 |
+
|
| 215 |
+
---
|
| 216 |
+
|
| 217 |
+
## 👥 Attribution & Contact
|
| 218 |
+
|
| 219 |
+
*Please provide attribution for the dataset creators and a point of contact.*
|
| 220 |
+
|
| 221 |
+
| | |
|
| 222 |
+
| :--- | :--- |
|
| 223 |
+
| **Dataset Lead** | `[Yunke Ao, Luohong Wu]` |
|
| 224 |
+
| **Institution** | `[Balgrist University Hospital]` |
|
| 225 |
+
| **Contact Email** | `[yunke.ao@balgrist.ch, luohong.wu@balgrist.ch, ...]` |
|
| 226 |
+
| **Citation (BibTeX)** | <pre><code>@misc{[Ultrabones100k_lerobot_2026],<br> author = {[Yunke Ao, Luohong Wu, Philipp Fuernstahl]},<br> title = {[Ultrabones100k Lerobot Dataset]},<br> year = {2026},<br> publisher = {Open-H-Embodiment},<br> note = {https://hrpp.research.virginia.edu/teams/irb-sbs/researcher-guide-irb-sbs/identifiers}<br>}</code></pre> |
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/episodes.jsonl
ADDED
|
@@ -0,0 +1,60 @@
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|
| 1 |
+
{"episode_index": 0, "tasks": ["scanning fibula with ultrasound"], "length": 1057}
|
| 2 |
+
{"episode_index": 1, "tasks": ["scanning fibula with ultrasound"], "length": 1135}
|
| 3 |
+
{"episode_index": 2, "tasks": ["scanning fibula with ultrasound"], "length": 1699}
|
| 4 |
+
{"episode_index": 3, "tasks": ["scanning fibula with ultrasound"], "length": 1501}
|
| 5 |
+
{"episode_index": 4, "tasks": ["scanning tibia with ultrasound"], "length": 1382}
|
| 6 |
+
{"episode_index": 5, "tasks": ["scanning tibia with ultrasound"], "length": 1614}
|
| 7 |
+
{"episode_index": 6, "tasks": ["scanning tibia with ultrasound"], "length": 1572}
|
| 8 |
+
{"episode_index": 7, "tasks": ["scanning tibia with ultrasound"], "length": 1528}
|
| 9 |
+
{"episode_index": 8, "tasks": ["scanning tibia with ultrasound"], "length": 1561}
|
| 10 |
+
{"episode_index": 9, "tasks": ["scanning fibula with ultrasound"], "length": 1439}
|
| 11 |
+
{"episode_index": 10, "tasks": ["scanning fibula with ultrasound"], "length": 1450}
|
| 12 |
+
{"episode_index": 11, "tasks": ["scanning fibula with ultrasound"], "length": 1600}
|
| 13 |
+
{"episode_index": 12, "tasks": ["scanning fibula with ultrasound"], "length": 1552}
|
| 14 |
+
{"episode_index": 13, "tasks": ["scanning tibia with ultrasound"], "length": 1478}
|
| 15 |
+
{"episode_index": 14, "tasks": ["scanning tibia with ultrasound"], "length": 1331}
|
| 16 |
+
{"episode_index": 15, "tasks": ["scanning tibia with ultrasound"], "length": 1339}
|
| 17 |
+
{"episode_index": 16, "tasks": ["scanning tibia with ultrasound"], "length": 1326}
|
| 18 |
+
{"episode_index": 17, "tasks": ["scanning fibula with ultrasound"], "length": 1254}
|
| 19 |
+
{"episode_index": 18, "tasks": ["scanning fibula with ultrasound"], "length": 1283}
|
| 20 |
+
{"episode_index": 19, "tasks": ["scanning fibula with ultrasound"], "length": 1346}
|
| 21 |
+
{"episode_index": 20, "tasks": ["scanning tibia with ultrasound"], "length": 1293}
|
| 22 |
+
{"episode_index": 21, "tasks": ["scanning tibia with ultrasound"], "length": 1261}
|
| 23 |
+
{"episode_index": 22, "tasks": ["scanning tibia with ultrasound"], "length": 1387}
|
| 24 |
+
{"episode_index": 23, "tasks": ["scanning tibia with ultrasound"], "length": 1267}
|
| 25 |
+
{"episode_index": 24, "tasks": ["scanning fibula with ultrasound"], "length": 1855}
|
| 26 |
+
{"episode_index": 25, "tasks": ["scanning fibula with ultrasound"], "length": 1762}
|
| 27 |
+
{"episode_index": 26, "tasks": ["scanning fibula with ultrasound"], "length": 1663}
|
| 28 |
+
{"episode_index": 27, "tasks": ["scanning tibia with ultrasound"], "length": 1496}
|
| 29 |
+
{"episode_index": 28, "tasks": ["scanning tibia with ultrasound"], "length": 1438}
|
| 30 |
+
{"episode_index": 29, "tasks": ["scanning tibia with ultrasound"], "length": 1511}
|
| 31 |
+
{"episode_index": 30, "tasks": ["scanning tibia with ultrasound"], "length": 1538}
|
| 32 |
+
{"episode_index": 31, "tasks": ["scanning fibula with ultrasound"], "length": 1646}
|
| 33 |
+
{"episode_index": 32, "tasks": ["scanning fibula with ultrasound"], "length": 1775}
|
| 34 |
+
{"episode_index": 33, "tasks": ["scanning fibula with ultrasound"], "length": 1793}
|
| 35 |
+
{"episode_index": 34, "tasks": ["scanning tibia with ultrasound"], "length": 1885}
|
| 36 |
+
{"episode_index": 35, "tasks": ["scanning tibia with ultrasound"], "length": 1751}
|
| 37 |
+
{"episode_index": 36, "tasks": ["scanning tibia with ultrasound"], "length": 1818}
|
| 38 |
+
{"episode_index": 37, "tasks": ["scanning fibula with ultrasound"], "length": 1082}
|
| 39 |
+
{"episode_index": 38, "tasks": ["scanning fibula with ultrasound"], "length": 948}
|
| 40 |
+
{"episode_index": 39, "tasks": ["scanning fibula with ultrasound"], "length": 1130}
|
| 41 |
+
{"episode_index": 40, "tasks": ["scanning tibia with ultrasound"], "length": 1390}
|
| 42 |
+
{"episode_index": 41, "tasks": ["scanning tibia with ultrasound"], "length": 1658}
|
| 43 |
+
{"episode_index": 42, "tasks": ["scanning tibia with ultrasound"], "length": 1687}
|
| 44 |
+
{"episode_index": 43, "tasks": ["scanning fibula with ultrasound"], "length": 1672}
|
| 45 |
+
{"episode_index": 44, "tasks": ["scanning fibula with ultrasound"], "length": 1669}
|
| 46 |
+
{"episode_index": 45, "tasks": ["scanning fibula with ultrasound"], "length": 1736}
|
| 47 |
+
{"episode_index": 46, "tasks": ["scanning tibia with ultrasound"], "length": 1337}
|
| 48 |
+
{"episode_index": 47, "tasks": ["scanning tibia with ultrasound"], "length": 1534}
|
| 49 |
+
{"episode_index": 48, "tasks": ["scanning tibia with ultrasound"], "length": 1648}
|
| 50 |
+
{"episode_index": 49, "tasks": ["scanning fibula with ultrasound"], "length": 1386}
|
| 51 |
+
{"episode_index": 50, "tasks": ["scanning fibula with ultrasound"], "length": 1464}
|
| 52 |
+
{"episode_index": 51, "tasks": ["scanning fibula with ultrasound"], "length": 1574}
|
| 53 |
+
{"episode_index": 52, "tasks": ["scanning tibia with ultrasound"], "length": 1643}
|
| 54 |
+
{"episode_index": 53, "tasks": ["scanning tibia with ultrasound"], "length": 1653}
|
| 55 |
+
{"episode_index": 54, "tasks": ["scanning tibia with ultrasound"], "length": 1719}
|
| 56 |
+
{"episode_index": 55, "tasks": ["scanning fibula with ultrasound"], "length": 1521}
|
| 57 |
+
{"episode_index": 56, "tasks": ["scanning fibula with ultrasound"], "length": 1505}
|
| 58 |
+
{"episode_index": 57, "tasks": ["scanning tibia with ultrasound"], "length": 1601}
|
| 59 |
+
{"episode_index": 58, "tasks": ["scanning tibia with ultrasound"], "length": 1655}
|
| 60 |
+
{"episode_index": 59, "tasks": ["scanning tibia with ultrasound"], "length": 1573}
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/episodes_stats.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
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|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/info.json
ADDED
|
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|
| 1 |
+
{
|
| 2 |
+
"codebase_version": "v2.1",
|
| 3 |
+
"robot_type": "us_image_corner",
|
| 4 |
+
"total_episodes": 60,
|
| 5 |
+
"total_frames": 90371,
|
| 6 |
+
"total_tasks": 2,
|
| 7 |
+
"total_videos": 60,
|
| 8 |
+
"total_chunks": 1,
|
| 9 |
+
"chunks_size": 1000,
|
| 10 |
+
"fps": 21,
|
| 11 |
+
"splits": {
|
| 12 |
+
"train": "0:60"
|
| 13 |
+
},
|
| 14 |
+
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
|
| 15 |
+
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
|
| 16 |
+
"features": {
|
| 17 |
+
"observation.state": {
|
| 18 |
+
"dtype": "float32",
|
| 19 |
+
"shape": [
|
| 20 |
+
6
|
| 21 |
+
],
|
| 22 |
+
"names": [
|
| 23 |
+
"x",
|
| 24 |
+
"y",
|
| 25 |
+
"z",
|
| 26 |
+
"euler_x",
|
| 27 |
+
"euler_y",
|
| 28 |
+
"euler_z"
|
| 29 |
+
]
|
| 30 |
+
},
|
| 31 |
+
"action": {
|
| 32 |
+
"dtype": "float32",
|
| 33 |
+
"shape": [
|
| 34 |
+
6
|
| 35 |
+
],
|
| 36 |
+
"names": [
|
| 37 |
+
"x",
|
| 38 |
+
"y",
|
| 39 |
+
"z",
|
| 40 |
+
"euler_x",
|
| 41 |
+
"euler_y",
|
| 42 |
+
"euler_z"
|
| 43 |
+
]
|
| 44 |
+
},
|
| 45 |
+
"observation.images.ultrasound": {
|
| 46 |
+
"dtype": "video",
|
| 47 |
+
"shape": [
|
| 48 |
+
811,
|
| 49 |
+
950,
|
| 50 |
+
3
|
| 51 |
+
],
|
| 52 |
+
"names": [
|
| 53 |
+
"height",
|
| 54 |
+
"width",
|
| 55 |
+
"channels"
|
| 56 |
+
],
|
| 57 |
+
"info": {
|
| 58 |
+
"video.height": 811,
|
| 59 |
+
"video.width": 950,
|
| 60 |
+
"video.codec": "av1",
|
| 61 |
+
"video.pix_fmt": "yuv420p",
|
| 62 |
+
"video.is_depth_map": false,
|
| 63 |
+
"video.fps": 21,
|
| 64 |
+
"video.channels": 3,
|
| 65 |
+
"has_audio": false
|
| 66 |
+
}
|
| 67 |
+
},
|
| 68 |
+
"instruction.text": {
|
| 69 |
+
"dtype": "string",
|
| 70 |
+
"shape": [
|
| 71 |
+
1
|
| 72 |
+
],
|
| 73 |
+
"description": "Natural language command for the robot"
|
| 74 |
+
},
|
| 75 |
+
"observation.meta.probe_type": {
|
| 76 |
+
"dtype": "string",
|
| 77 |
+
"shape": [
|
| 78 |
+
1
|
| 79 |
+
],
|
| 80 |
+
"names": [
|
| 81 |
+
"SuperSonic_Imagine_linear"
|
| 82 |
+
]
|
| 83 |
+
},
|
| 84 |
+
"observation.meta.probe_acquisition_param": {
|
| 85 |
+
"dtype": "float32",
|
| 86 |
+
"shape": [
|
| 87 |
+
4
|
| 88 |
+
],
|
| 89 |
+
"names": [
|
| 90 |
+
"center_frequency_mhz",
|
| 91 |
+
"num_elements",
|
| 92 |
+
"imaging_depth_cm",
|
| 93 |
+
"imaging_width_cm"
|
| 94 |
+
]
|
| 95 |
+
},
|
| 96 |
+
"timestamp": {
|
| 97 |
+
"dtype": "float32",
|
| 98 |
+
"shape": [
|
| 99 |
+
1
|
| 100 |
+
],
|
| 101 |
+
"names": null
|
| 102 |
+
},
|
| 103 |
+
"frame_index": {
|
| 104 |
+
"dtype": "int64",
|
| 105 |
+
"shape": [
|
| 106 |
+
1
|
| 107 |
+
],
|
| 108 |
+
"names": null
|
| 109 |
+
},
|
| 110 |
+
"episode_index": {
|
| 111 |
+
"dtype": "int64",
|
| 112 |
+
"shape": [
|
| 113 |
+
1
|
| 114 |
+
],
|
| 115 |
+
"names": null
|
| 116 |
+
},
|
| 117 |
+
"index": {
|
| 118 |
+
"dtype": "int64",
|
| 119 |
+
"shape": [
|
| 120 |
+
1
|
| 121 |
+
],
|
| 122 |
+
"names": null
|
| 123 |
+
},
|
| 124 |
+
"task_index": {
|
| 125 |
+
"dtype": "int64",
|
| 126 |
+
"shape": [
|
| 127 |
+
1
|
| 128 |
+
],
|
| 129 |
+
"names": null
|
| 130 |
+
}
|
| 131 |
+
}
|
| 132 |
+
}
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full/meta/tasks.jsonl
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"task_index": 0, "task": "scanning fibula with ultrasound"}
|
| 2 |
+
{"task_index": 1, "task": "scanning tibia with ultrasound"}
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/README.md
ADDED
|
@@ -0,0 +1,228 @@
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
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|
|
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|
|
|
|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
|
|
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|
|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
|
|
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|
|
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|
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|
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|
|
|
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|
|
|
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|
|
|
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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 |
+
# Ultrabones100k Cadaveric Ultrasound Dataset with synthetic robot state_2- README
|
| 8 |
+
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
## 📋 At a Glance
|
| 12 |
+
|
| 13 |
+
*Freehand tracked ultrasound scan from an expert surgeon, augmented with synthetic robot state with randomized base positions and ee to us extrinsics.*
|
| 14 |
+
|
| 15 |
+
---
|
| 16 |
+
|
| 17 |
+
## 📖 Dataset Overview
|
| 18 |
+
|
| 19 |
+
*This dataset contains 12 trajectories of expert surgeons using the ultrasound probe to scan bones, with different ultrasound image shape to the second one. The state is the current pose. Observation is the ultrasound image. The action is extracted as the delta cartesion pose to 1 second later.*
|
| 20 |
+
|
| 21 |
+
| | |
|
| 22 |
+
| :--- | :--- |
|
| 23 |
+
| **Total Trajectories** | `[12]` |
|
| 24 |
+
| **Total Hours** | `[0.24]` |
|
| 25 |
+
| **Data Type** | `[ ] Clinical` `[x] Ex-Vivo` `[ ] Table-Top Phantom` `[x] Digital Simulation` `[ ] Physical Simulation` `[ ] Other (If checked, update "Other")` |
|
| 26 |
+
| **License** | CC BY 4.0 |
|
| 27 |
+
| **Version** | `[e.g., 1.0]` |
|
| 28 |
+
|
| 29 |
+
---
|
| 30 |
+
|
| 31 |
+
## 🎯 Tasks & Domain
|
| 32 |
+
|
| 33 |
+
### Domain
|
| 34 |
+
|
| 35 |
+
*Select the primary domain for this dataset.*
|
| 36 |
+
|
| 37 |
+
- [ ] **Surgical Robotics**
|
| 38 |
+
- [x] **Ultrasound Robotics**
|
| 39 |
+
- [ ] **Other Healthcare Robotics** (Please specify: `[Your Domain]`)
|
| 40 |
+
|
| 41 |
+
### Demonstrated Skills
|
| 42 |
+
|
| 43 |
+
- Ultrasound scanning for orthopedics / reconstruction (fibula and tibia)
|
| 44 |
+
|
| 45 |
+
---
|
| 46 |
+
|
| 47 |
+
## 🔬 Data Collection Details
|
| 48 |
+
|
| 49 |
+
### Collection Method
|
| 50 |
+
|
| 51 |
+
*How was the data collected?*
|
| 52 |
+
|
| 53 |
+
- [ ] **Human Teleoperation**
|
| 54 |
+
- [ ] **Programmatic/State-Machine**
|
| 55 |
+
- [ ] **AI Policy / Autonomous**
|
| 56 |
+
- [x] **Other** (Please specify: `[Expert freehand]`)
|
| 57 |
+
|
| 58 |
+
### Operator Details
|
| 59 |
+
|
| 60 |
+
| | Description |
|
| 61 |
+
| :--- | :--- |
|
| 62 |
+
| **Operator Count** | `[1]` |
|
| 63 |
+
| **Operator Skill Level** | `[x] Expert (e.g., Surgeon, Sonographer)` <br> `[ ] Intermediate (e.g., Trained Researcher)` <br> `[ ] Novice (e.g., ML Researcher with minimal experience)` <br> `[ ] N/A` |
|
| 64 |
+
| **Collection Period** | From `[2024-01]` to `[2024-06]` |
|
| 65 |
+
|
| 66 |
+
### Recovery Demonstrations
|
| 67 |
+
|
| 68 |
+
*Does this dataset include examples of recovering from failure?*
|
| 69 |
+
|
| 70 |
+
- [ ] **Yes**
|
| 71 |
+
- [x] **No**
|
| 72 |
+
|
| 73 |
+
**If yes, please briefly describe the recovery process:**
|
| 74 |
+
|
| 75 |
+
---
|
| 76 |
+
|
| 77 |
+
## 💡 Diversity Dimensions
|
| 78 |
+
|
| 79 |
+
*Check all dimensions that were intentionally varied during data collection.*
|
| 80 |
+
|
| 81 |
+
- [x] **Camera Position / Angle**
|
| 82 |
+
- [ ] **Lighting Conditions**
|
| 83 |
+
- [x] **Target Object** (e.g., different phantom models, suture types)
|
| 84 |
+
- [x] **Spatial Layout** (e.g., placing the target suture needle in various locations)
|
| 85 |
+
- [ ] **Robot Embodiment** (if multiple robots were used)
|
| 86 |
+
- [x] **Task Execution** (e.g., different techniques for the same task)
|
| 87 |
+
- [ ] **Background / Scene**
|
| 88 |
+
- [ ] **Other** (Please specify: `[Your Dimension]`)
|
| 89 |
+
|
| 90 |
+
*If you checked any of the above please briefly elaborate below.*
|
| 91 |
+
|
| 92 |
+
We start the ultrasound scan from different initial positions. We scan multiple bone structures including fibla and tibia. We scan 14 different cadavers with 5-12 various recordings for each of them.
|
| 93 |
+
|
| 94 |
+
---
|
| 95 |
+
|
| 96 |
+
## 🛠️ Equipment & Setup
|
| 97 |
+
|
| 98 |
+
### Robotic Platform(s)
|
| 99 |
+
|
| 100 |
+
*Kuka med14*
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
### Sensors & Cameras
|
| 104 |
+
|
| 105 |
+
*List the sensors and cameras used. Specify model names where possible. (Add and remove rows as needed)*
|
| 106 |
+
|
| 107 |
+
| Type | Model/Details |
|
| 108 |
+
| :--- | :--- |
|
| 109 |
+
| **Primary Camera** | `[None]` |
|
| 110 |
+
| **Room/3rd Person Camera** | `[None]` |
|
| 111 |
+
| **Force/Torque Sensor** | `[None]` |
|
| 112 |
+
| **Medical Imager** | `[e.g., SuperSonic Imagine SL18-5 Ultrasound, B-Mode]` |
|
| 113 |
+
| **Other** | `[Specify]` |
|
| 114 |
+
|
| 115 |
+
---
|
| 116 |
+
|
| 117 |
+
## 🎯 Action & State Space Representation
|
| 118 |
+
|
| 119 |
+
*Describe how actions and robot states are represented in your dataset. This is crucial for understanding data compatibility and enabling effective policy learning.*
|
| 120 |
+
|
| 121 |
+
### Action Space Representation
|
| 122 |
+
|
| 123 |
+
**Primary Action Representation:**
|
| 124 |
+
- [ ] **Absolute Cartesian** (position/orientation relative to robot base)
|
| 125 |
+
- [x] **Relative Cartesian** (delta position/orientation from current pose)
|
| 126 |
+
- [ ] **Joint Space** (direct joint angle commands)
|
| 127 |
+
- [ ] **Other** (Please specify: `[Your Representation]`)
|
| 128 |
+
|
| 129 |
+
**Orientation Representation:**
|
| 130 |
+
- [x] **Quaternions** (x, y, z, w)
|
| 131 |
+
- [ ] **Euler Angles** (roll, pitch, yaw)
|
| 132 |
+
- [ ] **Axis-Angle** (rotation vector)
|
| 133 |
+
- [ ] **Rotation Matrix** (3x3 matrix)
|
| 134 |
+
- [ ] **Other** (Please specify: `None`)
|
| 135 |
+
|
| 136 |
+
**Reference Frame:**
|
| 137 |
+
- [ ] **Robot Base Frame**
|
| 138 |
+
- [x] **Tool/End-Effector Frame**
|
| 139 |
+
- [ ] **World/Global Frame**
|
| 140 |
+
- [ ] **Camera Frame**
|
| 141 |
+
- [ ] **Other** (Please specify: `[Your Frame]`)
|
| 142 |
+
|
| 143 |
+
**Action Dimensions:**
|
| 144 |
+
*List the action space dimensions and their meanings.*
|
| 145 |
+
```
|
| 146 |
+
action: [x, y, z, qw, qx, qy, qz]
|
| 147 |
+
- x, y, z: relative position in ultrasound image frame (meters) to 1 sec later
|
| 148 |
+
- qw, qx, qy, qz: quaternion in image frame to 1 sec later
|
| 149 |
+
```
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
**Example:**
|
| 153 |
+
```
|
| 154 |
+
action: [x, y, z, qx, qy, qz, qw, gripper]
|
| 155 |
+
- x, y, z: Absolute position in robot base frame (meters)
|
| 156 |
+
- qx, qy, qz, qw: Absolute orientation as quaternion
|
| 157 |
+
- gripper: Gripper opening angle (radians)
|
| 158 |
+
```
|
| 159 |
+
|
| 160 |
+
### State Space Representation
|
| 161 |
+
|
| 162 |
+
**State Information Included:**
|
| 163 |
+
- [x] **Joint Positions** (all articulated joints)
|
| 164 |
+
- [ ] **Joint Velocities**
|
| 165 |
+
- [x] **End-Effector Pose** (Cartesian position/orientation)
|
| 166 |
+
- [ ] **Force/Torque Readings**
|
| 167 |
+
- [ ] **Gripper State** (position, force, etc.)
|
| 168 |
+
- [ ] **Other** (Please specify: `[Your State Info]`)
|
| 169 |
+
|
| 170 |
+
**State Dimensions:**
|
| 171 |
+
*List the state space dimensions and their meanings.*
|
| 172 |
+
|
| 173 |
+
```
|
| 174 |
+
observation.state.us_pose: [x, y, z, qw, qx, qy, qz]
|
| 175 |
+
- x, y, z: Absolute position in robot base frame (meters)
|
| 176 |
+
- qw, qx, qy, qz: quaternion in robot base frame
|
| 177 |
+
observation.state.joint_pos: [j1, j2, ..., j7]
|
| 178 |
+
- j1-j7: joint positions
|
| 179 |
+
```
|
| 180 |
+
|
| 181 |
+
|
| 182 |
+
**Example:**
|
| 183 |
+
```
|
| 184 |
+
observation.state: [j1, j2, j3, j4, j5, j6, j7, gripper_pos]
|
| 185 |
+
- j1-j7: Absolute joint positions for 7-DOF arm (radians)
|
| 186 |
+
- gripper_pos: Current gripper opening (meters)
|
| 187 |
+
```
|
| 188 |
+
|
| 189 |
+
### 📋 Recommended Additional Representations
|
| 190 |
+
|
| 191 |
+
*Even if not your primary action/state representation, we strongly encourage including these standardized formats for maximum compatibility:*
|
| 192 |
+
|
| 193 |
+
**Recommended Action Fields:**
|
| 194 |
+
- **`action.cartesian_absolute`**: Absolute Cartesian pose with absolute quaternions
|
| 195 |
+
```
|
| 196 |
+
[x, y, z, qx, qy, qz, qw, gripper_angle]
|
| 197 |
+
```
|
| 198 |
+
|
| 199 |
+
**Recommended State Fields:**
|
| 200 |
+
- **`observation.state.joint_positions`**: Absolute positions for all articulated joints
|
| 201 |
+
```
|
| 202 |
+
[joint_1, joint_2, ..., joint_n]
|
| 203 |
+
```
|
| 204 |
+
|
| 205 |
+
|
| 206 |
+
---
|
| 207 |
+
|
| 208 |
+
## ⏱️ Data Synchronization Approach
|
| 209 |
+
|
| 210 |
+
|
| 211 |
+
*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.*
|
| 212 |
+
|
| 213 |
+
*The synchronization is part of of calibration procedure, which is detailed in [Ultrabones100k](https://arxiv.org/abs/2502.03783)*.
|
| 214 |
+
|
| 215 |
+
**Example:** *We collect joint kinematics from our Franka Research 3 and RGB-D frames from Intel RealSense D435 cameras, all running in ROS 2 Galactic 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, /camera/*/image_raw, and /camera/*/camera_info in a single rosbag2 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 ±2 ms across a 2-minute capture.*
|
| 216 |
+
|
| 217 |
+
---
|
| 218 |
+
|
| 219 |
+
## 👥 Attribution & Contact
|
| 220 |
+
|
| 221 |
+
*Please provide attribution for the dataset creators and a point of contact.*
|
| 222 |
+
|
| 223 |
+
| | |
|
| 224 |
+
| :--- | :--- |
|
| 225 |
+
| **Dataset Lead** | `[Yunke Ao, Luohong Wu]` |
|
| 226 |
+
| **Institution** | `[Balgrist University Hospital]` |
|
| 227 |
+
| **Contact Email** | `[yunke.ao@balgrist.ch, luohong.wu@balgrist.ch, ...]` |
|
| 228 |
+
| **Citation (BibTeX)** | <pre><code>@misc{[Ultrabones100k_lerobot_2026],<br> author = {[Yunke Ao, Luohong Wu, Philipp Fuernstahl]},<br> title = {[Ultrabones100k Lerobot Dataset]},<br> year = {2026},<br> publisher = {Open-H-Embodiment},<br> note = {https://hrpp.research.virginia.edu/teams/irb-sbs/researcher-guide-irb-sbs/identifiers}<br>}</code></pre> |
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/episodes.jsonl
ADDED
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|
| 1 |
+
{"episode_index": 0, "tasks": ["scanning fibula with ultrasound"], "length": 1792}
|
| 2 |
+
{"episode_index": 1, "tasks": ["scanning fibula with ultrasound"], "length": 1795}
|
| 3 |
+
{"episode_index": 2, "tasks": ["scanning fibula with ultrasound"], "length": 1923}
|
| 4 |
+
{"episode_index": 3, "tasks": ["scanning tibia with ultrasound"], "length": 1898}
|
| 5 |
+
{"episode_index": 4, "tasks": ["scanning tibia with ultrasound"], "length": 1803}
|
| 6 |
+
{"episode_index": 5, "tasks": ["scanning tibia with ultrasound"], "length": 1660}
|
| 7 |
+
{"episode_index": 6, "tasks": ["scanning fibula with ultrasound"], "length": 1734}
|
| 8 |
+
{"episode_index": 7, "tasks": ["scanning fibula with ultrasound"], "length": 1608}
|
| 9 |
+
{"episode_index": 8, "tasks": ["scanning fibula with ultrasound"], "length": 1638}
|
| 10 |
+
{"episode_index": 9, "tasks": ["scanning tibia with ultrasound"], "length": 1623}
|
| 11 |
+
{"episode_index": 10, "tasks": ["scanning tibia with ultrasound"], "length": 1508}
|
| 12 |
+
{"episode_index": 11, "tasks": ["scanning tibia with ultrasound"], "length": 1686}
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/episodes_stats.jsonl
ADDED
|
@@ -0,0 +1,12 @@
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|
| 1 |
+
{"episode_index": 0, "stats": {"action": {"min": [-0.014486627653241158, -0.020103145390748978, -0.09843277931213379, -0.10692143440246582, -0.08359620720148087, -0.06567657738924026], "max": [0.013099943287670612, 0.009474379010498524, 0.042376723140478134, 0.06293214112520218, 0.054873935878276825, 0.19616344571113586], "mean": [-0.0003298772207926959, -7.133166218409315e-05, 0.00033173715928569436, -0.00034664664417505264, -4.13018606195692e-05, 0.010930757038295269], "std": [0.003866615006700158, 0.0037484627682715654, 0.024534812197089195, 0.024307454004883766, 0.02125309780240059, 0.04249249026179314], "count": [1792]}, "observation.images.ultrasound": {"min": [[[0.0]], [[0.0]], [[0.0]]], "max": [[[0.9607843137254902]], [[0.9607843137254902]], [[0.9607843137254902]]], "mean": [[[0.15998460987974375]], [[0.1599803976169338]], [[0.15999691906391086]]], "std": [[[0.17663369525098357]], [[0.17664106460698364]], [[0.17662929573411632]]], "count": [275]}, "observation.meta.probe_acquisition_param": {"min": [11.5, 256.0, 4.46999979019165, 4.760000228881836], "max": [11.5, 256.0, 4.46999979019165, 4.760000228881836], "mean": [11.5, 256.0, 4.470091819763184, 4.759882926940918], "std": [0.0, 0.0, 9.202864748658612e-05, 0.00011730046389857307], "count": [1792]}, "timestamp": {"min": [0.0], "max": [85.28571428571429], "mean": [42.642857142857146], "std": [24.633607649922904], "count": [1792]}, "frame_index": {"min": [0], "max": [1791], "mean": [895.5], "std": [517.3057606483809], "count": [1792]}, "episode_index": {"min": [0], "max": [0], "mean": [0.0], "std": [0.0], "count": [1792]}, "index": {"min": [0], "max": [1791], "mean": [895.5], "std": [517.3057606483809], "count": [1792]}, "task_index": {"min": [0], "max": [0], "mean": [0.0], "std": [0.0], "count": [1792]}, "observation.state.us_pose": {"min": [-0.4918676018714905, 0.5403412580490112, -0.033807773143053055, -0.45650848746299744, -0.6402661204338074, -0.5368491411209106, -0.6564304232597351], "max": [-0.07437175512313843, 0.6792861223220825, 0.021021002903580666, 0.2358388751745224, 0.6964156627655029, 0.6422604918479919, 0.7167824506759644], "mean": [-0.2796246409416199, 0.6140732169151306, -0.00148826336953789, -0.1584516018629074, -0.16359925270080566, 0.25531303882598877, 0.2695786654949188], "std": [0.12460957467556, 0.03205704316496849, 0.014093386940658092, 0.18867935240268707, 0.533498227596283, 0.39223718643188477, 0.5798283219337463], "count": [1792]}, "observation.state.joint_pos": {"min": [1.578731894493103, 1.1926653385162354, 0.29392677545547485, -1.730165719985962, 0.863679051399231, -1.779319405555725, -3.0920493602752686], "max": [2.7314865589141846, 2.49094557762146, 1.3254215717315674, 1.7340929508209229, 2.513113498687744, -0.8004407286643982, -0.6044464707374573], "mean": [2.366236448287964, 1.9395087957382202, 0.9121655225753784, 0.3550121486186981, 1.8426759243011475, -1.2381330728530884, -2.118487596511841], "std": [0.3466184139251709, 0.4201641082763672, 0.38045555353164673, 1.1935304403305054, 0.5157278180122375, 0.21849550306797028, 0.8605340123176575], "count": [1792]}, "observation.meta.prob_cali_mtx": {"min": [0.0032587447203695774, 0.004612947814166546, 0.04187280684709549, 0.7071067690849304, -0.7071067690849304, 0.0, 0.0], "max": [0.0032587447203695774, 0.004612947814166546, 0.04187280684709549, 0.7071067690849304, -0.7071067690849304, 0.0, 0.0], "mean": [0.003258713288232684, 0.004612924996763468, 0.041872747242450714, 0.7071070671081543, -0.7071070671081543, 0.0, 0.0], "std": [3.14317176730583e-08, 2.2817403078079224e-08, 5.960464477539063e-08, 2.980232238769531e-07, 2.980232238769531e-07, 0.0, 0.0], "count": [1792]}}}
|
| 2 |
+
{"episode_index": 1, "stats": {"action": {"min": [-0.011632570065557957, -0.013356597162783146, -0.06641031056642532, -0.06416101008653641, -0.11396794021129608, -0.09467267990112305], "max": [0.019943412393331528, 0.009211321361362934, 0.0662519633769989, 0.06615347415208817, 0.05193731561303139, 0.3996885120868683], "mean": [6.54912946629338e-06, 0.0002662457700353116, -3.9633698179386556e-05, -0.0014068230520933867, 2.0721745386254042e-05, 0.011087118647992611], "std": [0.0036522806622087955, 0.0034913592971861362, 0.023150905966758728, 0.021016474813222885, 0.022012092173099518, 0.05198746547102928], "count": [1795]}, "observation.images.ultrasound": {"min": [[[0.0]], [[0.0]], [[0.0]]], "max": [[[0.9529411764705882]], [[0.9529411764705882]], [[0.9529411764705882]]], "mean": [[[0.15847371697733864]], [[0.1584784927194806]], [[0.15847438043663822]]], "std": [[[0.17716996119009037]], [[0.17716823100531195]], [[0.1771698649653519]]], "count": [275]}, "observation.meta.probe_acquisition_param": {"min": [11.5, 256.0, 4.46999979019165, 4.760000228881836], "max": [11.5, 256.0, 4.46999979019165, 4.760000228881836], "mean": [11.5, 256.0, 4.470092296600342, 4.75988245010376], "std": [0.0, 0.0, 9.25063795875758e-05, 0.00011777796316891909], "count": [1795]}, "timestamp": {"min": [0.0], "max": [85.42857142857143], "mean": [42.71428571428572], "std": [24.67484696127533], "count": [1795]}, "frame_index": {"min": [0], "max": [1794], "mean": [897.0], "std": [518.1717861867819], "count": [1795]}, "episode_index": {"min": [1], "max": [1], "mean": [1.0], "std": [0.0], "count": [1795]}, "index": {"min": [1792], "max": [3586], "mean": [2689.0], "std": [518.1717861867819], "count": [1795]}, "task_index": {"min": [0], "max": [0], "mean": [0.0], "std": [0.0], "count": [1795]}, "observation.state.us_pose": {"min": [-0.3922876715660095, 0.5202866196632385, -0.1343776434659958, -0.43028825521469116, -0.6643332242965698, -0.46262064576148987, -0.6642507314682007], "max": [0.024561159312725067, 0.6697478294372559, -0.07750625163316727, 0.1577705442905426, 0.7052714824676514, 0.651351809501648, 0.6824145913124084], "mean": [-0.1909731775522232, 0.5998150110244751, -0.09902377426624298, -0.18461358547210693, -0.23969170451164246, 0.30276545882225037, 0.3182201385498047], "std": [0.12650592625141144, 0.03367408737540245, 0.0159353818744421, 0.1841462254524231, 0.5122106671333313, 0.3706122636795044, 0.5309534072875977], "count": [1795]}, "observation.state.joint_pos": {"min": [1.0833688974380493, 1.388942003250122, 0.29347503185272217, -1.6966304779052734, 0.5109097361564636, -1.2081254720687866, -2.0464441776275635], "max": [2.031264543533325, 1.8390496969223022, 0.9415547251701355, -0.3739264905452728, 2.1213574409484863, -0.38299959897994995, -0.4696330428123474], "mean": [1.6298506259918213, 1.5753728151321411, 0.5579380393028259, -1.2314856052398682, 1.2950983047485352, -0.900410532951355, -1.2248728275299072], "std": [0.23481899499893188, 0.1018170565366745, 0.16423316299915314, 0.32717132568359375, 0.49994418025016785, 0.18163350224494934, 0.4751964807510376], "count": [1795]}, "observation.meta.prob_cali_mtx": {"min": [-0.00568854995071888, 0.007612799294292927, 0.05105573683977127, 0.7071067690849304, -0.7071067690849304, 0.0, 0.0], "max": [-0.00568854995071888, 0.007612799294292927, 0.05105573683977127, 0.7071067690849304, -0.7071067690849304, 0.0, 0.0], "mean": [-0.005688644014298916, 0.007612919434905052, 0.0510559007525444, 0.7071071267127991, -0.7071071267127991, 0.0, 0.0], "std": [9.406319634308602e-08, 1.2013903472052334e-07, 1.6391277313232422e-07, 3.5762786865234375e-07, 3.5762786865234375e-07, 0.0, 0.0], "count": [1795]}}}
|
| 3 |
+
{"episode_index": 2, "stats": {"action": {"min": [-0.011633498594164848, -0.012791577726602554, -0.08160058408975601, -0.11201398074626923, -0.0930684357881546, -0.05602875351905823], "max": [0.015019373036921024, 0.009094825014472008, 0.09096519649028778, 0.09378796815872192, 0.08916459232568741, 0.18045546114444733], "mean": [0.00019497002358548343, -2.8144273528596386e-05, 0.0003880400909110904, -0.0007281237049028277, 0.0010549366706982255, 0.010926011949777603], "std": [0.00377717730589211, 0.003272420261055231, 0.022284748032689095, 0.02859102189540863, 0.025251520797610283, 0.04501015320420265], "count": [1923]}, "observation.images.ultrasound": {"min": [[[0.0]], [[0.0]], [[0.0]]], "max": [[[0.9647058823529412]], [[0.9647058823529412]], [[0.9647058823529412]]], "mean": [[[0.15398174170651505]], [[0.15398365720822116]], [[0.15398254343740694]]], "std": [[[0.17229151985550012]], [[0.17229092366661614]], [[0.17229141149221955]]], "count": [290]}, "observation.meta.probe_acquisition_param": {"min": [11.5, 256.0, 4.46999979019165, 4.760000228881836], "max": [11.5, 256.0, 4.46999979019165, 4.760000228881836], "mean": [11.5, 256.0, 4.4700775146484375, 4.7598748207092285], "std": [0.0, 0.0, 7.772347453283146e-05, 0.00012540738680399954], "count": [1923]}, "timestamp": {"min": [0.0], "max": [91.52380952380952], "mean": [45.76190476190476], "std": [26.434390893680135], "count": [1923]}, "frame_index": {"min": [0], "max": [1922], "mean": [961.0], "std": [555.1222087672828], "count": [1923]}, "episode_index": {"min": [2], "max": [2], "mean": [2.0], "std": [0.0], "count": [1923]}, "index": {"min": [3587], "max": [5509], "mean": [4548.0], "std": [555.1222087672828], "count": [1923]}, "task_index": {"min": [0], "max": [0], "mean": [0.0], "std": [0.0], "count": [1923]}, "observation.state.us_pose": {"min": [-0.38272231817245483, 0.39966264367103577, -0.07602981477975845, -0.45932379364967346, -0.6512782573699951, -0.46268677711486816, -0.6709343791007996], "max": [0.025514673441648483, 0.5305569171905518, -0.014233475551009178, 0.15003696084022522, 0.6885455250740051, 0.6722164750099182, 0.6764360070228577], "mean": [-0.19582681357860565, 0.46451669931411743, -0.04282086715102196, -0.219180166721344, -0.1790716052055359, 0.31452369689941406, 0.26284268498420715], "std": [0.11754940450191498, 0.02984921634197235, 0.019037488847970963, 0.201632559299469, 0.5142862796783447, 0.3836127519607544, 0.5473359823226929], "count": [1923]}, "observation.state.joint_pos": {"min": [0.5826311111450195, 1.5850638151168823, 0.7401965260505676, -2.1338930130004883, 0.33788809180259705, -1.7372336387634277, -1.6461182832717896], "max": [1.7629036903381348, 1.9537971019744873, 1.4351328611373901, -1.074741244316101, 1.4764158725738525, -1.1731586456298828, -0.8269113898277283], "mean": [1.2405821084976196, 1.8221315145492554, 1.1428180932998657, -1.6915358304977417, 0.8598182797431946, -1.4740052223205566, -1.2778584957122803], "std": [0.32450351119041443, 0.08821278065443039, 0.195286825299263, 0.2678634226322174, 0.3450426161289215, 0.16602139174938202, 0.2568262815475464], "count": [1923]}, "observation.meta.prob_cali_mtx": {"min": [-0.004692156333476305, -0.004961785394698381, 0.06131827086210251, 0.7071067690849304, -0.7071067690849304, 0.0, 0.0], "max": [-0.004692156333476305, -0.004961785394698381, 0.06131827086210251, 0.7071067690849304, -0.7071067690849304, 0.0, 0.0], "mean": [-0.004692099057137966, -0.00496187387034297, 0.0613175667822361, 0.7071102261543274, -0.7071102261543274, 0.0, 0.0], "std": [5.727553542556052e-08, 8.847548116364123e-08, 7.04072419921431e-07, 3.4570693969726562e-06, 3.4570693969726562e-06, 0.0, 0.0], "count": [1923]}}}
|
| 4 |
+
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| 5 |
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| 6 |
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|
| 7 |
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| 8 |
+
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|
| 9 |
+
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|
| 10 |
+
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| 11 |
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| 12 |
+
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|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/info.json
ADDED
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@@ -0,0 +1,163 @@
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| 1 |
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{
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| 2 |
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| 3 |
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| 11 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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| 20 |
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| 21 |
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| 23 |
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| 24 |
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| 26 |
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| 30 |
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| 31 |
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| 56 |
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| 57 |
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| 58 |
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| 59 |
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|
| 60 |
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| 62 |
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|
| 163 |
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|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full_2_synthetic_robot_2/meta/tasks.jsonl
ADDED
|
@@ -0,0 +1,2 @@
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|
| 1 |
+
{"task_index": 0, "task": "scanning fibula with ultrasound"}
|
| 2 |
+
{"task_index": 1, "task": "scanning tibia with ultrasound"}
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full_3_synthetic_robot_2/meta/README.md
ADDED
|
@@ -0,0 +1,228 @@
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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 |
+
# Ultrabones100k Cadaveric Ultrasound Dataset with synthetic robot state_3- README
|
| 8 |
+
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
## 📋 At a Glance
|
| 12 |
+
|
| 13 |
+
*Freehand tracked ultrasound scan from an expert surgeon, augmented with synthetic robot state with randomized base positions and ee to us extrinsics.*
|
| 14 |
+
|
| 15 |
+
---
|
| 16 |
+
|
| 17 |
+
## 📖 Dataset Overview
|
| 18 |
+
|
| 19 |
+
*This dataset contains 18 trajectories of expert surgeons using the ultrasound probe to scan bones, with different ultrasound image shape to the second one. The state is the current pose. Observation is the ultrasound image. The action is extracted as the delta cartesion pose to 1 second later.*
|
| 20 |
+
|
| 21 |
+
| | |
|
| 22 |
+
| :--- | :--- |
|
| 23 |
+
| **Total Trajectories** | `[18]` |
|
| 24 |
+
| **Total Hours** | `[0.36]` |
|
| 25 |
+
| **Data Type** | `[ ] Clinical` `[x] Ex-Vivo` `[ ] Table-Top Phantom` `[x] Digital Simulation` `[ ] Physical Simulation` `[ ] Other (If checked, update "Other")` |
|
| 26 |
+
| **License** | CC BY 4.0 |
|
| 27 |
+
| **Version** | `[e.g., 1.0]` |
|
| 28 |
+
|
| 29 |
+
---
|
| 30 |
+
|
| 31 |
+
## 🎯 Tasks & Domain
|
| 32 |
+
|
| 33 |
+
### Domain
|
| 34 |
+
|
| 35 |
+
*Select the primary domain for this dataset.*
|
| 36 |
+
|
| 37 |
+
- [ ] **Surgical Robotics**
|
| 38 |
+
- [x] **Ultrasound Robotics**
|
| 39 |
+
- [ ] **Other Healthcare Robotics** (Please specify: `[Your Domain]`)
|
| 40 |
+
|
| 41 |
+
### Demonstrated Skills
|
| 42 |
+
|
| 43 |
+
- Ultrasound scanning for orthopedics / reconstruction (fibula and tibia)
|
| 44 |
+
|
| 45 |
+
---
|
| 46 |
+
|
| 47 |
+
## 🔬 Data Collection Details
|
| 48 |
+
|
| 49 |
+
### Collection Method
|
| 50 |
+
|
| 51 |
+
*How was the data collected?*
|
| 52 |
+
|
| 53 |
+
- [ ] **Human Teleoperation**
|
| 54 |
+
- [ ] **Programmatic/State-Machine**
|
| 55 |
+
- [ ] **AI Policy / Autonomous**
|
| 56 |
+
- [x] **Other** (Please specify: `[Expert freehand]`)
|
| 57 |
+
|
| 58 |
+
### Operator Details
|
| 59 |
+
|
| 60 |
+
| | Description |
|
| 61 |
+
| :--- | :--- |
|
| 62 |
+
| **Operator Count** | `[1]` |
|
| 63 |
+
| **Operator Skill Level** | `[x] Expert (e.g., Surgeon, Sonographer)` <br> `[ ] Intermediate (e.g., Trained Researcher)` <br> `[ ] Novice (e.g., ML Researcher with minimal experience)` <br> `[ ] N/A` |
|
| 64 |
+
| **Collection Period** | From `[2024-01]` to `[2024-06]` |
|
| 65 |
+
|
| 66 |
+
### Recovery Demonstrations
|
| 67 |
+
|
| 68 |
+
*Does this dataset include examples of recovering from failure?*
|
| 69 |
+
|
| 70 |
+
- [ ] **Yes**
|
| 71 |
+
- [x] **No**
|
| 72 |
+
|
| 73 |
+
**If yes, please briefly describe the recovery process:**
|
| 74 |
+
|
| 75 |
+
---
|
| 76 |
+
|
| 77 |
+
## 💡 Diversity Dimensions
|
| 78 |
+
|
| 79 |
+
*Check all dimensions that were intentionally varied during data collection.*
|
| 80 |
+
|
| 81 |
+
- [x] **Camera Position / Angle**
|
| 82 |
+
- [ ] **Lighting Conditions**
|
| 83 |
+
- [x] **Target Object** (e.g., different phantom models, suture types)
|
| 84 |
+
- [x] **Spatial Layout** (e.g., placing the target suture needle in various locations)
|
| 85 |
+
- [ ] **Robot Embodiment** (if multiple robots were used)
|
| 86 |
+
- [x] **Task Execution** (e.g., different techniques for the same task)
|
| 87 |
+
- [ ] **Background / Scene**
|
| 88 |
+
- [ ] **Other** (Please specify: `[Your Dimension]`)
|
| 89 |
+
|
| 90 |
+
*If you checked any of the above please briefly elaborate below.*
|
| 91 |
+
|
| 92 |
+
We start the ultrasound scan from different initial positions. We scan multiple bone structures including fibla and tibia. We scan 14 different cadavers with 5-12 various recordings for each of them.
|
| 93 |
+
|
| 94 |
+
---
|
| 95 |
+
|
| 96 |
+
## 🛠️ Equipment & Setup
|
| 97 |
+
|
| 98 |
+
### Robotic Platform(s)
|
| 99 |
+
|
| 100 |
+
*Kuka med14*
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
### Sensors & Cameras
|
| 104 |
+
|
| 105 |
+
*List the sensors and cameras used. Specify model names where possible. (Add and remove rows as needed)*
|
| 106 |
+
|
| 107 |
+
| Type | Model/Details |
|
| 108 |
+
| :--- | :--- |
|
| 109 |
+
| **Primary Camera** | `[None]` |
|
| 110 |
+
| **Room/3rd Person Camera** | `[None]` |
|
| 111 |
+
| **Force/Torque Sensor** | `[None]` |
|
| 112 |
+
| **Medical Imager** | `[e.g., SuperSonic Imagine SL18-5 Ultrasound, B-Mode]` |
|
| 113 |
+
| **Other** | `[Specify]` |
|
| 114 |
+
|
| 115 |
+
---
|
| 116 |
+
|
| 117 |
+
## 🎯 Action & State Space Representation
|
| 118 |
+
|
| 119 |
+
*Describe how actions and robot states are represented in your dataset. This is crucial for understanding data compatibility and enabling effective policy learning.*
|
| 120 |
+
|
| 121 |
+
### Action Space Representation
|
| 122 |
+
|
| 123 |
+
**Primary Action Representation:**
|
| 124 |
+
- [ ] **Absolute Cartesian** (position/orientation relative to robot base)
|
| 125 |
+
- [x] **Relative Cartesian** (delta position/orientation from current pose)
|
| 126 |
+
- [ ] **Joint Space** (direct joint angle commands)
|
| 127 |
+
- [ ] **Other** (Please specify: `[Your Representation]`)
|
| 128 |
+
|
| 129 |
+
**Orientation Representation:**
|
| 130 |
+
- [x] **Quaternions** (x, y, z, w)
|
| 131 |
+
- [ ] **Euler Angles** (roll, pitch, yaw)
|
| 132 |
+
- [ ] **Axis-Angle** (rotation vector)
|
| 133 |
+
- [ ] **Rotation Matrix** (3x3 matrix)
|
| 134 |
+
- [ ] **Other** (Please specify: `None`)
|
| 135 |
+
|
| 136 |
+
**Reference Frame:**
|
| 137 |
+
- [ ] **Robot Base Frame**
|
| 138 |
+
- [x] **Tool/End-Effector Frame**
|
| 139 |
+
- [ ] **World/Global Frame**
|
| 140 |
+
- [ ] **Camera Frame**
|
| 141 |
+
- [ ] **Other** (Please specify: `[Your Frame]`)
|
| 142 |
+
|
| 143 |
+
**Action Dimensions:**
|
| 144 |
+
*List the action space dimensions and their meanings.*
|
| 145 |
+
```
|
| 146 |
+
action: [x, y, z, qw, qx, qy, qz]
|
| 147 |
+
- x, y, z: relative position in ultrasound image frame (meters) to 1 sec later
|
| 148 |
+
- qw, qx, qy, qz: quaternion in image frame to 1 sec later
|
| 149 |
+
```
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
**Example:**
|
| 153 |
+
```
|
| 154 |
+
action: [x, y, z, qx, qy, qz, qw, gripper]
|
| 155 |
+
- x, y, z: Absolute position in robot base frame (meters)
|
| 156 |
+
- qx, qy, qz, qw: Absolute orientation as quaternion
|
| 157 |
+
- gripper: Gripper opening angle (radians)
|
| 158 |
+
```
|
| 159 |
+
|
| 160 |
+
### State Space Representation
|
| 161 |
+
|
| 162 |
+
**State Information Included:**
|
| 163 |
+
- [x] **Joint Positions** (all articulated joints)
|
| 164 |
+
- [ ] **Joint Velocities**
|
| 165 |
+
- [x] **End-Effector Pose** (Cartesian position/orientation)
|
| 166 |
+
- [ ] **Force/Torque Readings**
|
| 167 |
+
- [ ] **Gripper State** (position, force, etc.)
|
| 168 |
+
- [ ] **Other** (Please specify: `[Your State Info]`)
|
| 169 |
+
|
| 170 |
+
**State Dimensions:**
|
| 171 |
+
*List the state space dimensions and their meanings.*
|
| 172 |
+
|
| 173 |
+
```
|
| 174 |
+
observation.state.us_pose: [x, y, z, qw, qx, qy, qz]
|
| 175 |
+
- x, y, z: Absolute position in robot base frame (meters)
|
| 176 |
+
- qw, qx, qy, qz: quaternion in robot base frame
|
| 177 |
+
observation.state.joint_pos: [j1, j2, ..., j7]
|
| 178 |
+
- j1-j7: joint positions
|
| 179 |
+
```
|
| 180 |
+
|
| 181 |
+
|
| 182 |
+
**Example:**
|
| 183 |
+
```
|
| 184 |
+
observation.state: [j1, j2, j3, j4, j5, j6, j7, gripper_pos]
|
| 185 |
+
- j1-j7: Absolute joint positions for 7-DOF arm (radians)
|
| 186 |
+
- gripper_pos: Current gripper opening (meters)
|
| 187 |
+
```
|
| 188 |
+
|
| 189 |
+
### 📋 Recommended Additional Representations
|
| 190 |
+
|
| 191 |
+
*Even if not your primary action/state representation, we strongly encourage including these standardized formats for maximum compatibility:*
|
| 192 |
+
|
| 193 |
+
**Recommended Action Fields:**
|
| 194 |
+
- **`action.cartesian_absolute`**: Absolute Cartesian pose with absolute quaternions
|
| 195 |
+
```
|
| 196 |
+
[x, y, z, qx, qy, qz, qw, gripper_angle]
|
| 197 |
+
```
|
| 198 |
+
|
| 199 |
+
**Recommended State Fields:**
|
| 200 |
+
- **`observation.state.joint_positions`**: Absolute positions for all articulated joints
|
| 201 |
+
```
|
| 202 |
+
[joint_1, joint_2, ..., joint_n]
|
| 203 |
+
```
|
| 204 |
+
|
| 205 |
+
|
| 206 |
+
---
|
| 207 |
+
|
| 208 |
+
## ⏱️ Data Synchronization Approach
|
| 209 |
+
|
| 210 |
+
|
| 211 |
+
*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.*
|
| 212 |
+
|
| 213 |
+
*The synchronization is part of of calibration procedure, which is detailed in [Ultrabones100k](https://arxiv.org/abs/2502.03783)*.
|
| 214 |
+
|
| 215 |
+
**Example:** *We collect joint kinematics from our Franka Research 3 and RGB-D frames from Intel RealSense D435 cameras, all running in ROS 2 Galactic 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, /camera/*/image_raw, and /camera/*/camera_info in a single rosbag2 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 ±2 ms across a 2-minute capture.*
|
| 216 |
+
|
| 217 |
+
---
|
| 218 |
+
|
| 219 |
+
## 👥 Attribution & Contact
|
| 220 |
+
|
| 221 |
+
*Please provide attribution for the dataset creators and a point of contact.*
|
| 222 |
+
|
| 223 |
+
| | |
|
| 224 |
+
| :--- | :--- |
|
| 225 |
+
| **Dataset Lead** | `[Yunke Ao, Luohong Wu]` |
|
| 226 |
+
| **Institution** | `[Balgrist University Hospital]` |
|
| 227 |
+
| **Contact Email** | `[yunke.ao@balgrist.ch, luohong.wu@balgrist.ch, ...]` |
|
| 228 |
+
| **Citation (BibTeX)** | <pre><code>@misc{[Ultrabones100k_lerobot_2026],<br> author = {[Yunke Ao, Luohong Wu, Philipp Fuernstahl]},<br> title = {[Ultrabones100k Lerobot Dataset]},<br> year = {2026},<br> publisher = {Open-H-Embodiment},<br> note = {https://hrpp.research.virginia.edu/teams/irb-sbs/researcher-guide-irb-sbs/identifiers}<br>}</code></pre> |
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full_3_synthetic_robot_2/meta/episodes.jsonl
ADDED
|
@@ -0,0 +1,18 @@
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|
| 1 |
+
{"episode_index": 0, "tasks": ["scanning fibula with ultrasound"], "length": 1336}
|
| 2 |
+
{"episode_index": 1, "tasks": ["scanning fibula with ultrasound"], "length": 1350}
|
| 3 |
+
{"episode_index": 2, "tasks": ["scanning fibula with ultrasound"], "length": 1362}
|
| 4 |
+
{"episode_index": 3, "tasks": ["scanning tibia with ultrasound"], "length": 861}
|
| 5 |
+
{"episode_index": 4, "tasks": ["scanning tibia with ultrasound"], "length": 1057}
|
| 6 |
+
{"episode_index": 5, "tasks": ["scanning tibia with ultrasound"], "length": 839}
|
| 7 |
+
{"episode_index": 6, "tasks": ["scanning fibula with ultrasound"], "length": 1448}
|
| 8 |
+
{"episode_index": 7, "tasks": ["scanning fibula with ultrasound"], "length": 1642}
|
| 9 |
+
{"episode_index": 8, "tasks": ["scanning fibula with ultrasound"], "length": 1598}
|
| 10 |
+
{"episode_index": 9, "tasks": ["scanning tibia with ultrasound"], "length": 1673}
|
| 11 |
+
{"episode_index": 10, "tasks": ["scanning tibia with ultrasound"], "length": 1674}
|
| 12 |
+
{"episode_index": 11, "tasks": ["scanning tibia with ultrasound"], "length": 1612}
|
| 13 |
+
{"episode_index": 12, "tasks": ["scanning fibula with ultrasound"], "length": 1097}
|
| 14 |
+
{"episode_index": 13, "tasks": ["scanning fibula with ultrasound"], "length": 952}
|
| 15 |
+
{"episode_index": 14, "tasks": ["scanning fibula with ultrasound"], "length": 925}
|
| 16 |
+
{"episode_index": 15, "tasks": ["scanning tibia with ultrasound"], "length": 1471}
|
| 17 |
+
{"episode_index": 16, "tasks": ["scanning tibia with ultrasound"], "length": 1572}
|
| 18 |
+
{"episode_index": 17, "tasks": ["scanning tibia with ultrasound"], "length": 1635}
|
Ultrasound/balgrist/ultrabones_lerobot_dataset_full_3_synthetic_robot_2/meta/info.json
ADDED
|
@@ -0,0 +1,163 @@
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| 1 |
+
{
|
| 2 |
+
"codebase_version": "v2.1",
|
| 3 |
+
"robot_type": null,
|
| 4 |
+
"total_episodes": 18,
|
| 5 |
+
"total_frames": 24104,
|
| 6 |
+
"total_tasks": 2,
|
| 7 |
+
"total_videos": 18,
|
| 8 |
+
"total_chunks": 1,
|
| 9 |
+
"chunks_size": 1000,
|
| 10 |
+
"fps": 21,
|
| 11 |
+
"splits": {
|
| 12 |
+
"train": "0:18"
|
| 13 |
+
},
|
| 14 |
+
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
|
| 15 |
+
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
|
| 16 |
+
"features": {
|
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