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Browse files- Surgical/jhu/imerse/endosrt_extracted/endosrt/meta/README.md +209 -0
- Surgical/jhu/imerse/endosrt_extracted/endosrt/meta/episodes.jsonl +0 -0
- Surgical/jhu/imerse/endosrt_extracted/endosrt/meta/episodes_stats.jsonl +0 -0
- Surgical/jhu/imerse/endosrt_extracted/endosrt/meta/info.json +124 -0
- Surgical/jhu/imerse/endosrt_extracted/endosrt/meta/tasks.jsonl +4 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/README.md +174 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/episodes.jsonl +0 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/episodes_stats.jsonl +0 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/info.json +226 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/modality.json +54 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/percentile_stats.json +0 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/relative_stats.json +1 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/stats.json +242 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/tasks.jsonl +4 -0
- Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/temporal_stats.json +0 -0
- Surgical/jhu/imerse/wound_closure/point_labeled/fausto_0_1_jesse_0_1_2_labeled/meta/relative_stats.json +1 -0
- Surgical/jhu/imerse/wound_closure/point_labeled/fausto_0_1_jesse_0_1_2_labeled/meta/stats.json +242 -0
- Surgical/jhu/lcsr/arcade/cholecystectomy/meta/README.md +316 -0
- Surgical/jhu/lcsr/arcade/cholecystectomy/meta/episodes.jsonl +750 -0
- Surgical/jhu/lcsr/arcade/cholecystectomy/meta/info.json +226 -0
Surgical/jhu/imerse/endosrt_extracted/endosrt/meta/README.md
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| 1 |
+
<!--
|
| 2 |
+
Open-H Embodiment Dataset README Template (v1.0)
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| 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 |
+
# [Soft Robotic Guidewire Navigation] - README
|
| 8 |
+
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
## 📋 At a Glance
|
| 12 |
+
|
| 13 |
+
*Teleoperated demonstrations of a 5mm-diameter pneumatic soft robotic guidewire navigating to aneurysms in 3D-printed, rigid, planar phantoms.*
|
| 14 |
+
---
|
| 15 |
+
|
| 16 |
+
## 📖 Dataset Overview
|
| 17 |
+
|
| 18 |
+
*Briefly describe the purpose and content of this dataset. What key skills or scenarios does it demonstrate?*
|
| 19 |
+
*This dataset contains 1907 trajectories of a single student demonstrator driving a soft robot's tip point into aneurysm cavities, in addition to 140 trajectories
|
| 20 |
+
executed by an ACT-based imitation learning policy. There are 36 geometries used for teleoperation and 6 for the autonomous policy rollout, each with two
|
| 21 |
+
aneurysms. Experiments are conducted on a table-top with simulated fluoroscopy as image feedback. It includes successful trials and recovery attempts.*
|
| 22 |
+
|
| 23 |
+
| | |
|
| 24 |
+
| :--- | :--- |
|
| 25 |
+
| **Total Trajectories** | `[2047]` |
|
| 26 |
+
| **Total Hours** | `[2.1]` |
|
| 27 |
+
| **Data Type** | `[ ] Clinical` `[ ] Ex-Vivo` `[x] Table-Top Phantom` `[ ] Digital Simulation` `[ ] Physical Simulation` `[ ] Other (If checked, update "Other")` |
|
| 28 |
+
| **License** | CC BY 4.0 |
|
| 29 |
+
| **Version** | `[e.g., 1.0]` |
|
| 30 |
+
|
| 31 |
+
---
|
| 32 |
+
|
| 33 |
+
## 🎯 Tasks & Domain
|
| 34 |
+
|
| 35 |
+
### Domain
|
| 36 |
+
|
| 37 |
+
*Select the primary domain for this dataset.*
|
| 38 |
+
|
| 39 |
+
- [x] **Surgical Robotics**
|
| 40 |
+
- [ ] **Ultrasound Robotics**
|
| 41 |
+
- [ ] **Other Healthcare Robotics** (Please specify: `[]`)
|
| 42 |
+
|
| 43 |
+
### Demonstrated Skills
|
| 44 |
+
|
| 45 |
+
*List the primary skills or procedures demonstrated in this dataset.*
|
| 46 |
+
- Advancing along vessel paths
|
| 47 |
+
- Selecting branches at vascular bifurcations
|
| 48 |
+
- Positioning inside aneurysm
|
| 49 |
+
|
| 50 |
+
---
|
| 51 |
+
|
| 52 |
+
## 🔬 Data Collection Details
|
| 53 |
+
|
| 54 |
+
### Collection Method
|
| 55 |
+
|
| 56 |
+
*How was the data collected?*
|
| 57 |
+
|
| 58 |
+
- [x] **Human Teleoperation**
|
| 59 |
+
- [ ] **Programmatic/State-Machine**
|
| 60 |
+
- [x] **AI Policy / Autonomous**
|
| 61 |
+
- [ ] **Other** (Please specify: `[Your Method]`)
|
| 62 |
+
|
| 63 |
+
### Operator Details
|
| 64 |
+
|
| 65 |
+
| | Description |
|
| 66 |
+
| :--- | :--- |
|
| 67 |
+
| **Operator Count** | `[1]` |
|
| 68 |
+
| **Operator Skill Level** | `[ ] Expert (e.g., Surgeon, Sonographer)` <br> `[ ] Intermediate (e.g., Trained Researcher)` <br> `[x] Novice (e.g., ML Researcher with minimal experience)` <br> `[ ] N/A` |
|
| 69 |
+
| **Collection Period** | From `[2025-03-01]` to `[2025-04-30]` |
|
| 70 |
+
|
| 71 |
+
### Recovery Demonstrations
|
| 72 |
+
|
| 73 |
+
*Does this dataset include examples of recovering from failure?*
|
| 74 |
+
|
| 75 |
+
- [x] **Yes**
|
| 76 |
+
- [ ] **No**
|
| 77 |
+
|
| 78 |
+
**If yes, please briefly describe the recovery process:**
|
| 79 |
+
|
| 80 |
+
*For 602 demonstrations, demonstrations are initialized from a failed robot position, the operator tries to drive it back to the intended path.*
|
| 81 |
+
|
| 82 |
+
---
|
| 83 |
+
|
| 84 |
+
## 💡 Diversity Dimensions
|
| 85 |
+
|
| 86 |
+
*Check all dimensions that were intentionally varied during data collection.*
|
| 87 |
+
|
| 88 |
+
- [ ] **Camera Position / Angle**
|
| 89 |
+
- [ ] **Lighting Conditions**
|
| 90 |
+
- [x] **Target Object** (e.g., different phantom models, suture types)
|
| 91 |
+
- [x] **Spatial Layout** (e.g., placing the target suture needle in various locations)
|
| 92 |
+
- [ ] **Robot Embodiment** (if multiple robots were used)
|
| 93 |
+
- [ ] **Task Execution** (e.g., different techniques for the same task)
|
| 94 |
+
- [ ] **Background / Scene**
|
| 95 |
+
- [ ] **Other** (Please specify: `[Your Dimension]`)
|
| 96 |
+
|
| 97 |
+
*If you checked any of the above please briefly elaborate below.*
|
| 98 |
+
|
| 99 |
+
We used 42 unique phantom geometries. In each of the different geometries, the robot starting position and aneurysm locations were slightly different.
|
| 100 |
+
|
| 101 |
+
---
|
| 102 |
+
|
| 103 |
+
## 🛠️ Equipment & Setup
|
| 104 |
+
|
| 105 |
+
### Robotic Platform(s)
|
| 106 |
+
|
| 107 |
+
*List the primary robot(s) used.*
|
| 108 |
+
|
| 109 |
+
- **Robot 1:** `Custon 3D-printed pneumatic soft robotic guidewire`
|
| 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 |
+
| **Primary Camera** | `[Basler a2A2448-75ucBAS, 612x512 @ 25fps]` |
|
| 118 |
+
| **Pressure Sensor** | `[Elveflow MPS-V2-L-4]` |
|
| 119 |
+
|
| 120 |
+
---
|
| 121 |
+
|
| 122 |
+
## 🎯 Action & State Space Representation
|
| 123 |
+
|
| 124 |
+
*Describe how actions and robot states are represented in your dataset. This is crucial for understanding data compatibility and enabling effective policy learning.*
|
| 125 |
+
|
| 126 |
+
### Action Space Representation
|
| 127 |
+
|
| 128 |
+
**Primary Action Representation:**
|
| 129 |
+
- [ ] **Absolute Cartesian** (position/orientation relative to robot base)
|
| 130 |
+
- [ ] **Relative Cartesian** (delta position/orientation from current pose)
|
| 131 |
+
- [x] **Joint Space** (direct joint angle commands)
|
| 132 |
+
- [ ] **Other** (Please specify: `[Your Representation]`)
|
| 133 |
+
|
| 134 |
+
**Orientation Representation:**
|
| 135 |
+
- [ ] **Quaternions** (x, y, z, w)
|
| 136 |
+
- [ ] **Euler Angles** (roll, pitch, yaw)
|
| 137 |
+
- [ ] **Axis-Angle** (rotation vector)
|
| 138 |
+
- [ ] **Rotation Matrix** (3x3 matrix)
|
| 139 |
+
- [x] **Other** (Please specify: `[None]`)
|
| 140 |
+
|
| 141 |
+
**Reference Frame:**
|
| 142 |
+
- [ ] **Robot Base Frame**
|
| 143 |
+
- [ ] **Tool/End-Effector Frame**
|
| 144 |
+
- [ ] **World/Global Frame**
|
| 145 |
+
- [ ] **Camera Frame**
|
| 146 |
+
- [x] **Other** (Please specify: `[None]`)
|
| 147 |
+
|
| 148 |
+
**Action Dimensions:**
|
| 149 |
+
*List the action space dimensions and their meanings.*
|
| 150 |
+
|
| 151 |
+
action: [bend_pos, translate_pos, contrast]
|
| 152 |
+
- bend_pos: Absolute position of stepper motor lead screw that drives syringe to induce bending (mL)
|
| 153 |
+
- translate_pos: Absolute position of stepper motor lead screw position that drives translation of robot's tube (mm)
|
| 154 |
+
- contrast: Binary flag (0/1) indicating whether to initiate a contrast injection
|
| 155 |
+
|
| 156 |
+
### State Space Representation
|
| 157 |
+
|
| 158 |
+
**State Information Included:**
|
| 159 |
+
- [x] **Joint Positions** (all articulated joints)
|
| 160 |
+
- [ ] **Joint Velocities**
|
| 161 |
+
- [ ] **End-Effector Pose** (Cartesian position/orientation)
|
| 162 |
+
- [ ] **Force/Torque Readings**
|
| 163 |
+
- [ ] **Gripper State** (position, force, etc.)
|
| 164 |
+
- [x] **Other** (Please specify: `[Pressure reading]`)
|
| 165 |
+
|
| 166 |
+
**State Dimensions:**
|
| 167 |
+
*List the state space dimensions and their meanings.*
|
| 168 |
+
|
| 169 |
+
observation.state: [bend_pos, translate_pos, bend_pressure]
|
| 170 |
+
- bend_pos: Absolute position of stepper motor lead screw that drives syringe to induce bending (mL)
|
| 171 |
+
- translate_pos: Absolute position of stepper motor lead screw position that drives translation of robot's tube (mm)
|
| 172 |
+
- bend_pressure: Differential pressure of the robot's internal pneumatic channel (mbar)
|
| 173 |
+
|
| 174 |
+
### 📋 Recommended Additional Representations
|
| 175 |
+
|
| 176 |
+
*Even if not your primary action/state representation, we strongly encourage including these standardized formats for maximum compatibility:*
|
| 177 |
+
|
| 178 |
+
**Recommended Action Fields:**
|
| 179 |
+
- **`action.cartesian_absolute`**: Absolute Cartesian pose with absolute quaternions
|
| 180 |
+
```
|
| 181 |
+
[x, y, z, qx, qy, qz, qw, gripper_angle]
|
| 182 |
+
```
|
| 183 |
+
|
| 184 |
+
**Recommended State Fields:**
|
| 185 |
+
- **`observation.state.joint_positions`**: Absolute positions for all articulated joints
|
| 186 |
+
```
|
| 187 |
+
[joint_1, joint_2, ..., joint_n]
|
| 188 |
+
```
|
| 189 |
+
|
| 190 |
+
|
| 191 |
+
---
|
| 192 |
+
|
| 193 |
+
## ⏱️ Data Synchronization Approach
|
| 194 |
+
|
| 195 |
+
*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.*
|
| 196 |
+
|
| 197 |
+
*We collect image frames from the Basler camera, pressure readings from the Elveflow, and motor positions from the stepper motors in each iteration of the same software control loop in LabVIEW software. The control loop ran at 25 Hz, and offline checks show skew of ±1 ms across a 5 minute capture. Thus, the camera, pressure readings, and motor positions are guaranteed to be within a 41 ms window. During export to LeRobot, the timestep's timestamp relative to the beginning of the run is written verbatim into the timestamp attribute.
|
| 198 |
+
---
|
| 199 |
+
|
| 200 |
+
## 👥 Attribution & Contact
|
| 201 |
+
|
| 202 |
+
*Please provide attribution for the dataset creators and a point of contact.*
|
| 203 |
+
|
| 204 |
+
| | |
|
| 205 |
+
| :--- | :--- |
|
| 206 |
+
| **Dataset Lead** | `[Noah Barnes]` |
|
| 207 |
+
| **Institution** | `[Johns Hopkins University]` |
|
| 208 |
+
| **Contact Email** | `[nbarne18@jhu.edu]` |
|
| 209 |
+
| **Citation (BibTeX)** | <pre><code>@misc{[endosrt],<br> author = {Noah Barnes},<br> title = {Soft Robotic Guidewire Navigation},<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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Surgical/jhu/imerse/endosrt_extracted/endosrt/meta/info.json
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"codebase_version": "v2.1",
|
| 3 |
+
"robot_type": "softrobot",
|
| 4 |
+
"total_episodes": 2047,
|
| 5 |
+
"total_frames": 193203,
|
| 6 |
+
"total_tasks": 4,
|
| 7 |
+
"total_videos": 4094,
|
| 8 |
+
"total_chunks": 3,
|
| 9 |
+
"chunks_size": 1000,
|
| 10 |
+
"fps": 25,
|
| 11 |
+
"splits": {
|
| 12 |
+
"train": "0:1000",
|
| 13 |
+
"test": "1000:1305",
|
| 14 |
+
"recovery": "1305:1907",
|
| 15 |
+
"autonomous": "1907:2047"
|
| 16 |
+
},
|
| 17 |
+
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
|
| 18 |
+
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
|
| 19 |
+
"features": {
|
| 20 |
+
"observation.fluoro_image": {
|
| 21 |
+
"dtype": "video",
|
| 22 |
+
"shape": [
|
| 23 |
+
512,
|
| 24 |
+
612,
|
| 25 |
+
3
|
| 26 |
+
],
|
| 27 |
+
"names": [
|
| 28 |
+
"height",
|
| 29 |
+
"width",
|
| 30 |
+
"channel"
|
| 31 |
+
],
|
| 32 |
+
"info": {
|
| 33 |
+
"video.height": 512,
|
| 34 |
+
"video.width": 612,
|
| 35 |
+
"video.codec": "av1",
|
| 36 |
+
"video.pix_fmt": "yuv420p",
|
| 37 |
+
"video.is_depth_map": false,
|
| 38 |
+
"video.fps": 25,
|
| 39 |
+
"video.channels": 3,
|
| 40 |
+
"has_audio": false
|
| 41 |
+
}
|
| 42 |
+
},
|
| 43 |
+
"observation.goal_image": {
|
| 44 |
+
"dtype": "video",
|
| 45 |
+
"shape": [
|
| 46 |
+
512,
|
| 47 |
+
612,
|
| 48 |
+
3
|
| 49 |
+
],
|
| 50 |
+
"names": [
|
| 51 |
+
"height",
|
| 52 |
+
"width",
|
| 53 |
+
"channel"
|
| 54 |
+
],
|
| 55 |
+
"info": {
|
| 56 |
+
"video.height": 512,
|
| 57 |
+
"video.width": 612,
|
| 58 |
+
"video.codec": "av1",
|
| 59 |
+
"video.pix_fmt": "yuv420p",
|
| 60 |
+
"video.is_depth_map": false,
|
| 61 |
+
"video.fps": 25,
|
| 62 |
+
"video.channels": 3,
|
| 63 |
+
"has_audio": false
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"observation.state": {
|
| 67 |
+
"dtype": "float32",
|
| 68 |
+
"shape": [
|
| 69 |
+
3
|
| 70 |
+
],
|
| 71 |
+
"names": [
|
| 72 |
+
"bend_pos",
|
| 73 |
+
"translate_pos",
|
| 74 |
+
"bend_pressure"
|
| 75 |
+
]
|
| 76 |
+
},
|
| 77 |
+
"action": {
|
| 78 |
+
"dtype": "float32",
|
| 79 |
+
"shape": [
|
| 80 |
+
3
|
| 81 |
+
],
|
| 82 |
+
"names": [
|
| 83 |
+
"bend_pos",
|
| 84 |
+
"translate_pos",
|
| 85 |
+
"contrast"
|
| 86 |
+
]
|
| 87 |
+
},
|
| 88 |
+
"timestamp": {
|
| 89 |
+
"dtype": "float32",
|
| 90 |
+
"shape": [
|
| 91 |
+
1
|
| 92 |
+
],
|
| 93 |
+
"names": null
|
| 94 |
+
},
|
| 95 |
+
"frame_index": {
|
| 96 |
+
"dtype": "int64",
|
| 97 |
+
"shape": [
|
| 98 |
+
1
|
| 99 |
+
],
|
| 100 |
+
"names": null
|
| 101 |
+
},
|
| 102 |
+
"episode_index": {
|
| 103 |
+
"dtype": "int64",
|
| 104 |
+
"shape": [
|
| 105 |
+
1
|
| 106 |
+
],
|
| 107 |
+
"names": null
|
| 108 |
+
},
|
| 109 |
+
"index": {
|
| 110 |
+
"dtype": "int64",
|
| 111 |
+
"shape": [
|
| 112 |
+
1
|
| 113 |
+
],
|
| 114 |
+
"names": null
|
| 115 |
+
},
|
| 116 |
+
"task_index": {
|
| 117 |
+
"dtype": "int64",
|
| 118 |
+
"shape": [
|
| 119 |
+
1
|
| 120 |
+
],
|
| 121 |
+
"names": null
|
| 122 |
+
}
|
| 123 |
+
}
|
| 124 |
+
}
|
Surgical/jhu/imerse/endosrt_extracted/endosrt/meta/tasks.jsonl
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"task_index": 0, "task": "advance"}
|
| 2 |
+
{"task_index": 1, "task": "turn left"}
|
| 3 |
+
{"task_index": 2, "task": "enter aneurysm"}
|
| 4 |
+
{"task_index": 3, "task": "turn right"}
|
Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/README.md
ADDED
|
@@ -0,0 +1,174 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
# NephFat - README
|
| 3 |
+
|
| 4 |
+
---
|
| 5 |
+
|
| 6 |
+
## 📋 At a Glance
|
| 7 |
+
|
| 8 |
+
NephFat is a vision-kinematics dataset for perinephric fat dissection in robot-assisted partial nephrectomy, capturing >2,000 trajectories on ex-vivo porcine kidneys using the da Vinci Research Kit-Si (dVRK-Si) and the daVinci Si system.
|
| 9 |
+
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
## 📖 Dataset Overview
|
| 13 |
+
|
| 14 |
+
This proposal introduces a focused, high-quality dataset capturing perinephric fat dissection performed on the da Vinci Si surgical system controlled via the da Vinci Research Kit-Si (dVRK-Si). The task demands precise tissue manipulation, coordinated bimanual tool use, and continuous spatial reasoning. This hierarchical structure supports research in subtask segmentation, skill learning, and long-horizon surgical planning.
|
| 15 |
+
|
| 16 |
+
| | |
|
| 17 |
+
| :--- | :--- |
|
| 18 |
+
| **Total Trajectories** | >2,000 |
|
| 19 |
+
| **Total Hours** | |
|
| 20 |
+
| **Data Type** | [ ] Clinical <br> [x] Ex-Vivo <br> [ ] Table-Top Phantom <br> [ ] Digital Simulation <br> [ ] Physical Simulation <br> [ ] Other (If checked, update "Other") |
|
| 21 |
+
| **License** | CC BY 4.0 |
|
| 22 |
+
| **Version** | 1.0 (Target Public Release: Mar 2026) |
|
| 23 |
+
|
| 24 |
+
---
|
| 25 |
+
|
| 26 |
+
## 🎯 Tasks & Domain
|
| 27 |
+
|
| 28 |
+
### Domain
|
| 29 |
+
|
| 30 |
+
- [x] **Surgical Robotics**
|
| 31 |
+
- [ ] **Ultrasound Robotics**
|
| 32 |
+
- [ ] **Other Healthcare Robotics** (Please specify: `[Your Domain]`)
|
| 33 |
+
|
| 34 |
+
### Demonstrated Skills
|
| 35 |
+
|
| 36 |
+
- Flap Grasp (tissue grasping and retraction)
|
| 37 |
+
- Scissors Placing (cutting tool positioning and alignment)
|
| 38 |
+
- Cut (controlled tissue dissection)
|
| 39 |
+
- Cap Removal (removal of fat overlying the tumor; optional)
|
| 40 |
+
|
| 41 |
+
---
|
| 42 |
+
|
| 43 |
+
## 🔬 Data Collection Details
|
| 44 |
+
|
| 45 |
+
### Collection Method
|
| 46 |
+
|
| 47 |
+
- [x] **Human Teleoperation**
|
| 48 |
+
- [ ] **Programmatic/State-Machine**
|
| 49 |
+
- [ ] **AI Policy / Autonomous**
|
| 50 |
+
- [ ] **Other** (Please specify: `[Your Method]`)
|
| 51 |
+
|
| 52 |
+
### Operator Details
|
| 53 |
+
|
| 54 |
+
| | Description |
|
| 55 |
+
| :--- | :--- |
|
| 56 |
+
| **Operator Count** | 3 (Doan Xuan Viet Pham, Dr. Jiawei Ge, Ethan Kilmer) |
|
| 57 |
+
| **Operator Skill Level** | [ ] Expert (e.g., Surgeon, Sonographer) <br> [x] Intermediate (e.g., Trained Researcher) <br> [x] Novice (e.g., ML Researcher with minimal experience) <br> [ ] N/A
|
| 58 |
+
| **Collection Period** | From 2025-06-01 to 2025-12-31 |
|
| 59 |
+
|
| 60 |
+
### Recovery Demonstrations
|
| 61 |
+
|
| 62 |
+
- [x] **Yes**
|
| 63 |
+
- [ ] **No**
|
| 64 |
+
|
| 65 |
+
**If yes, please briefly describe the recovery process:**
|
| 66 |
+
|
| 67 |
+
Recovery data was specifically collected for failed scissors placing; in these instances, the 'out-of-distribution' state—where scissors were misaligned behind or adjacent to the grasped flap rather than correctly positioned for the cut—was deliberately reproduced before recording the corrective recovery trajectory.
|
| 68 |
+
|
| 69 |
+
---
|
| 70 |
+
|
| 71 |
+
## 💡 Diversity Dimensions
|
| 72 |
+
|
| 73 |
+
- [ ] **Camera Position / Angle**
|
| 74 |
+
- [ ] **Lighting Conditions**
|
| 75 |
+
- [x] **Target Object** (e.g., different phantom models, suture types)
|
| 76 |
+
- [x] **Spatial Layout** (e.g., placing the target suture needle in various locations)
|
| 77 |
+
- [ ] **Robot Embodiment** (if multiple robots were used)
|
| 78 |
+
- [ ] **Task Execution** (e.g., different techniques for the same task)
|
| 79 |
+
- [ ] **Background / Scene**
|
| 80 |
+
- [x] **Other** (Please specify: `Cap Removal`)
|
| 81 |
+
|
| 82 |
+
*If you checked any of the above please briefly elaborate below.*
|
| 83 |
+
|
| 84 |
+
**Target Object:** The dataset comprises trajectories from $\ge20$ unique tissue samples. Trials were conducted on ex-vivo porcine kidneys prepared with chemically engineered tumor mimics (agarose and cellulose composites).
|
| 85 |
+
|
| 86 |
+
**Spatial Layout:** The prepared tumor mimics vary in size, shape and location.
|
| 87 |
+
|
| 88 |
+
**Cap Removal:**
|
| 89 |
+
Once adequate exposure is achieved, **cap removal is performed optionally**. Cap removal depends on surgeon preference and if existent after fat dissection. Some surgeons retain the fat cap as a grasping handle during subsequent tumor resection.
|
| 90 |
+
|
| 91 |
+
---
|
| 92 |
+
|
| 93 |
+
## 🛠️ Equipment & Setup
|
| 94 |
+
|
| 95 |
+
### Robotic Platform(s)
|
| 96 |
+
|
| 97 |
+
- **Robot 1:** da Vinci Si system controlled using the da Vinci Research Kit-Si (dVRK-Si)
|
| 98 |
+
|
| 99 |
+
### Sensors & Cameras
|
| 100 |
+
|
| 101 |
+
| Type | Model/Details |
|
| 102 |
+
| :--- | :--- |
|
| 103 |
+
| **Primary Camera** | Stereo endoscopic RGB camera |
|
| 104 |
+
| **Room/3rd Person Camera** | - |
|
| 105 |
+
| **Force/Torque Sensor** | - |
|
| 106 |
+
| **Medical Imager** | - |
|
| 107 |
+
| **Other** | Wrist-mounted RGB cameras (left and right arms) |
|
| 108 |
+
| **Other** | Robot kinematics and action trajectories |
|
| 109 |
+
|
| 110 |
+
---
|
| 111 |
+
|
| 112 |
+
## 🎯 Action & State Space Representation
|
| 113 |
+
|
| 114 |
+
### Action Space Representation
|
| 115 |
+
|
| 116 |
+
**Primary Action Representation:**
|
| 117 |
+
- [ ] **Absolute Cartesian** (position/orientation relative to robot base)
|
| 118 |
+
- [x] **Relative Cartesian** (delta position/orientation from current pose)
|
| 119 |
+
- [ ] **Joint Space** (direct joint angle commands)
|
| 120 |
+
- [ ] **Other** (Please specify: `[Your Representation]`)
|
| 121 |
+
|
| 122 |
+
**Orientation Representation:**
|
| 123 |
+
- [ ] **Quaternions** (x, y, z, w)
|
| 124 |
+
- [ ] **Euler Angles** (roll, pitch, yaw)
|
| 125 |
+
- [ ] **Axis-Angle** (rotation vector)
|
| 126 |
+
- [ ] **Rotation Matrix** (3x3 matrix)
|
| 127 |
+
- [x] **Other** (Please specify: `6D rotation`)
|
| 128 |
+
|
| 129 |
+
**Reference Frame:**
|
| 130 |
+
- [ ] **Robot Base Frame**
|
| 131 |
+
- [ ] **Tool/End-Effector Frame**
|
| 132 |
+
- [ ] **World/Global Frame**
|
| 133 |
+
- [ ] **Camera Frame**
|
| 134 |
+
- [x] **Other** (Please specify: `Hybrid: Position w.r.t Endoscope Camera Tip; Rotation w.r.t End-Effector`)
|
| 135 |
+
|
| 136 |
+
**Action Dimensions:**
|
| 137 |
+
10-dim action vector for each arm: [dx, dy, dz, r1, r2, r3, r4, r5, r6, jaw]
|
| 138 |
+
- dx, dy, dz: Delta position relative to Endoscope Tip Frame (3 dim)
|
| 139 |
+
- r1-r6: Delta rotation relative to current End-Effector Frame (6D rotation)
|
| 140 |
+
- jaw: Jaw angle (1 dim)
|
| 141 |
+
|
| 142 |
+
### State Space Representation
|
| 143 |
+
|
| 144 |
+
**State Information Included:**
|
| 145 |
+
- [x] **Joint Positions** (all articulated joints)
|
| 146 |
+
- [ ] **Joint Velocities**
|
| 147 |
+
- [x] **End-Effector Pose** (Cartesian position/orientation)
|
| 148 |
+
- [ ] **Force/Torque Readings**
|
| 149 |
+
- [x] **Gripper State** (position, force, etc.)
|
| 150 |
+
- [x] **Other** (Please specify: `Set Points (_sp), RCM Poses, and Setup Joints (suj)`)
|
| 151 |
+
|
| 152 |
+
**State Dimensions:**
|
| 153 |
+
*Comprehensive CSV state available (psm1, psm2, ecm, suj).
|
| 154 |
+
Key dimensions per arm:*
|
| 155 |
+
- **Joints:** 6 dim (`psm*_js[0-5]`)
|
| 156 |
+
- **Pose:** 7 dim (`position.x/y/z` + `orientation.x/y/z/w`)
|
| 157 |
+
- **Gripper:** 1 dim (`psm*_jaw`)
|
| 158 |
+
|
| 159 |
+
---
|
| 160 |
+
|
| 161 |
+
## ⏱️ Data Synchronization Approach
|
| 162 |
+
|
| 163 |
+
All sensor streams are time-synchronized, capturing continuous visual observations alongside corresponding robot actions. Quality assurance steps include verification of temporal alignment across modalities and consistency checks for kinematic and image streams.
|
| 164 |
+
|
| 165 |
+
---
|
| 166 |
+
|
| 167 |
+
## 👥 Attribution & Contact
|
| 168 |
+
|
| 169 |
+
| | |
|
| 170 |
+
| :--- | :--- |
|
| 171 |
+
| **Dataset Lead** | Doan Xuan Viet Pham, Dr. Jiawei Ge, Ethan Kilmer |
|
| 172 |
+
| **Institution** | Johns Hopkins University, Technical University of Munich |
|
| 173 |
+
| **Contact Email** | viet.x.pham@tum.de, jge9@jhu.edu, ekilmer1@jhu.edu |
|
| 174 |
+
| **Citation (BibTeX)** | <pre><code>@misc{nephfat_2026,<br> author = {Pham, Doan Xuan Viet and Ge, Jiawei and Kilmer, Ethan and Krieger, Axel},<br> title = {NephFat: A Vision-Kinematics Dataset for Perinephric Fat Dissection in Robot-Assisted Partial Nephrectomy},<br> year = {2026},<br> publisher = {Open-H-Embodiment},<br>}</code></pre> |
|
Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/episodes.jsonl
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Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/episodes_stats.jsonl
ADDED
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Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/info.json
ADDED
|
@@ -0,0 +1,226 @@
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|
| 1 |
+
{
|
| 2 |
+
"codebase_version": "v2.1",
|
| 3 |
+
"robot_type": "dvrk",
|
| 4 |
+
"total_episodes": 2112,
|
| 5 |
+
"total_frames": 494525,
|
| 6 |
+
"total_tasks": 4,
|
| 7 |
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"total_videos": 8448,
|
| 8 |
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"total_chunks": 3,
|
| 9 |
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"chunks_size": 1000,
|
| 10 |
+
"fps": 30,
|
| 11 |
+
"splits": {
|
| 12 |
+
"train": "0:1689",
|
| 13 |
+
"val": "1689:1900",
|
| 14 |
+
"test": "1900:2112",
|
| 15 |
+
"perfect": "0:2092",
|
| 16 |
+
"recovery": "2092:2112"
|
| 17 |
+
},
|
| 18 |
+
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
|
| 19 |
+
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
|
| 20 |
+
"features": {
|
| 21 |
+
"observation.images.endoscope.left": {
|
| 22 |
+
"dtype": "video",
|
| 23 |
+
"shape": [
|
| 24 |
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540,
|
| 25 |
+
960,
|
| 26 |
+
3
|
| 27 |
+
],
|
| 28 |
+
"names": [
|
| 29 |
+
"height",
|
| 30 |
+
"width",
|
| 31 |
+
"channel"
|
| 32 |
+
],
|
| 33 |
+
"info": {
|
| 34 |
+
"video.height": 540,
|
| 35 |
+
"video.width": 960,
|
| 36 |
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"video.codec": "av1",
|
| 37 |
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"video.pix_fmt": "yuv420p",
|
| 38 |
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"video.is_depth_map": false,
|
| 39 |
+
"video.fps": 30,
|
| 40 |
+
"video.channels": 3,
|
| 41 |
+
"has_audio": false
|
| 42 |
+
}
|
| 43 |
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},
|
| 44 |
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"observation.images.endoscope.right": {
|
| 45 |
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"dtype": "video",
|
| 46 |
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"shape": [
|
| 47 |
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540,
|
| 48 |
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960,
|
| 49 |
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3
|
| 50 |
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],
|
| 51 |
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"names": [
|
| 52 |
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"height",
|
| 53 |
+
"width",
|
| 54 |
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"channel"
|
| 55 |
+
],
|
| 56 |
+
"info": {
|
| 57 |
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"video.height": 540,
|
| 58 |
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"video.width": 960,
|
| 59 |
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"video.codec": "av1",
|
| 60 |
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"video.pix_fmt": "yuv420p",
|
| 61 |
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"video.is_depth_map": false,
|
| 62 |
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"video.fps": 30,
|
| 63 |
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"video.channels": 3,
|
| 64 |
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"has_audio": false
|
| 65 |
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}
|
| 66 |
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},
|
| 67 |
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"observation.images.wrist.left": {
|
| 68 |
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"dtype": "video",
|
| 69 |
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"shape": [
|
| 70 |
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480,
|
| 71 |
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640,
|
| 72 |
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3
|
| 73 |
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],
|
| 74 |
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"names": [
|
| 75 |
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"height",
|
| 76 |
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"width",
|
| 77 |
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"channel"
|
| 78 |
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],
|
| 79 |
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"info": {
|
| 80 |
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"video.height": 480,
|
| 81 |
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"video.width": 640,
|
| 82 |
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"video.codec": "av1",
|
| 83 |
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"video.pix_fmt": "yuv420p",
|
| 84 |
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"video.is_depth_map": false,
|
| 85 |
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"video.fps": 30,
|
| 86 |
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"video.channels": 3,
|
| 87 |
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"has_audio": false
|
| 88 |
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}
|
| 89 |
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},
|
| 90 |
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"observation.images.wrist.right": {
|
| 91 |
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"dtype": "video",
|
| 92 |
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"shape": [
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| 93 |
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|
| 94 |
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640,
|
| 95 |
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3
|
| 96 |
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],
|
| 97 |
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"names": [
|
| 98 |
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"height",
|
| 99 |
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"width",
|
| 100 |
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"channel"
|
| 101 |
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],
|
| 102 |
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"info": {
|
| 103 |
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"video.height": 480,
|
| 104 |
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|
| 105 |
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"video.codec": "av1",
|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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"video.channels": 3,
|
| 110 |
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|
| 111 |
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}
|
| 112 |
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},
|
| 113 |
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"observation.state": {
|
| 114 |
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"dtype": "float32",
|
| 115 |
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"shape": [
|
| 116 |
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16
|
| 117 |
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],
|
| 118 |
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"names": [
|
| 119 |
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[
|
| 120 |
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"psm1_pose.position.x",
|
| 121 |
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"psm1_pose.position.y",
|
| 122 |
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"psm1_pose.position.z",
|
| 123 |
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"psm1_pose.orientation.x",
|
| 124 |
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"psm1_pose.orientation.y",
|
| 125 |
+
"psm1_pose.orientation.z",
|
| 126 |
+
"psm1_pose.orientation.w",
|
| 127 |
+
"psm1_jaw",
|
| 128 |
+
"psm2_pose.position.x",
|
| 129 |
+
"psm2_pose.position.y",
|
| 130 |
+
"psm2_pose.position.z",
|
| 131 |
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"psm2_pose.orientation.x",
|
| 132 |
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"psm2_pose.orientation.y",
|
| 133 |
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"psm2_pose.orientation.z",
|
| 134 |
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"psm2_pose.orientation.w",
|
| 135 |
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"psm2_jaw"
|
| 136 |
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]
|
| 137 |
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]
|
| 138 |
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},
|
| 139 |
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"action": {
|
| 140 |
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"dtype": "float32",
|
| 141 |
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|
| 142 |
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16
|
| 143 |
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],
|
| 144 |
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"names": [
|
| 145 |
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[
|
| 146 |
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"psm1_sp.position.x",
|
| 147 |
+
"psm1_sp.position.y",
|
| 148 |
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"psm1_sp.position.z",
|
| 149 |
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"psm1_sp.orientation.x",
|
| 150 |
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"psm1_sp.orientation.y",
|
| 151 |
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"psm1_sp.orientation.z",
|
| 152 |
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"psm1_sp.orientation.w",
|
| 153 |
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"psm1_jaw_sp",
|
| 154 |
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"psm2_sp.position.x",
|
| 155 |
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"psm2_sp.position.y",
|
| 156 |
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"psm2_sp.position.z",
|
| 157 |
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"psm2_sp.orientation.x",
|
| 158 |
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"psm2_sp.orientation.y",
|
| 159 |
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"psm2_sp.orientation.z",
|
| 160 |
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"psm2_sp.orientation.w",
|
| 161 |
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"psm2_jaw_sp"
|
| 162 |
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]
|
| 163 |
+
]
|
| 164 |
+
},
|
| 165 |
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"observation.meta.tool.psm1": {
|
| 166 |
+
"dtype": "string",
|
| 167 |
+
"shape": [
|
| 168 |
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1
|
| 169 |
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],
|
| 170 |
+
"names": [
|
| 171 |
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"value"
|
| 172 |
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]
|
| 173 |
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},
|
| 174 |
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"observation.meta.tool.psm2": {
|
| 175 |
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"dtype": "string",
|
| 176 |
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|
| 177 |
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1
|
| 178 |
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],
|
| 179 |
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"names": [
|
| 180 |
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"value"
|
| 181 |
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]
|
| 182 |
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},
|
| 183 |
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"instruction.text": {
|
| 184 |
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"dtype": "string",
|
| 185 |
+
"shape": [
|
| 186 |
+
1
|
| 187 |
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],
|
| 188 |
+
"description": "Natural language command for the robot"
|
| 189 |
+
},
|
| 190 |
+
"timestamp": {
|
| 191 |
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"dtype": "float32",
|
| 192 |
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|
| 193 |
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1
|
| 194 |
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],
|
| 195 |
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|
| 196 |
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},
|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
+
1
|
| 201 |
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],
|
| 202 |
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|
| 203 |
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},
|
| 204 |
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"episode_index": {
|
| 205 |
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"dtype": "int64",
|
| 206 |
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|
| 207 |
+
1
|
| 208 |
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],
|
| 209 |
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|
| 210 |
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},
|
| 211 |
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"index": {
|
| 212 |
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"dtype": "int64",
|
| 213 |
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|
| 214 |
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1
|
| 215 |
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],
|
| 216 |
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|
| 217 |
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},
|
| 218 |
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"task_index": {
|
| 219 |
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"dtype": "int64",
|
| 220 |
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|
| 221 |
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1
|
| 222 |
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|
| 223 |
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|
| 224 |
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}
|
| 225 |
+
}
|
| 226 |
+
}
|
Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/modality.json
ADDED
|
@@ -0,0 +1,54 @@
|
|
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|
| 1 |
+
{
|
| 2 |
+
"state": {
|
| 3 |
+
"psm1_pose": {
|
| 4 |
+
"start": 0,
|
| 5 |
+
"end": 7
|
| 6 |
+
},
|
| 7 |
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|
| 8 |
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"start": 7,
|
| 9 |
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"end": 8
|
| 10 |
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},
|
| 11 |
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"psm2_pose": {
|
| 12 |
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|
| 13 |
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"end": 15
|
| 14 |
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},
|
| 15 |
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|
| 16 |
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|
| 17 |
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"end": 16
|
| 18 |
+
}
|
| 19 |
+
},
|
| 20 |
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"action": {
|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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},
|
| 25 |
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"psm1_gripper": {
|
| 26 |
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|
| 27 |
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|
| 28 |
+
},
|
| 29 |
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"psm2_pose": {
|
| 30 |
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"start": 8,
|
| 31 |
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"end": 15
|
| 32 |
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},
|
| 33 |
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"psm2_gripper": {
|
| 34 |
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"start": 15,
|
| 35 |
+
"end": 16
|
| 36 |
+
}
|
| 37 |
+
},
|
| 38 |
+
"video": {
|
| 39 |
+
"endoscope_left": {
|
| 40 |
+
"original_key": "observation.images.endoscope.left"
|
| 41 |
+
},
|
| 42 |
+
"wrist_left": {
|
| 43 |
+
"original_key": "observation.images.wrist.left"
|
| 44 |
+
},
|
| 45 |
+
"wrist_right": {
|
| 46 |
+
"original_key": "observation.images.wrist.right"
|
| 47 |
+
}
|
| 48 |
+
},
|
| 49 |
+
"annotation": {
|
| 50 |
+
"human.task_description": {
|
| 51 |
+
"original_key": "task_index"
|
| 52 |
+
}
|
| 53 |
+
}
|
| 54 |
+
}
|
Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/percentile_stats.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/relative_stats.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{}
|
Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/stats.json
ADDED
|
@@ -0,0 +1,242 @@
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Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/tasks.jsonl
ADDED
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@@ -0,0 +1,4 @@
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|
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|
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|
Surgical/jhu/imerse/nephfat_extracted/nephfat/meta/temporal_stats.json
ADDED
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The diff for this file is too large to render.
See raw diff
|
|
|
Surgical/jhu/imerse/wound_closure/point_labeled/fausto_0_1_jesse_0_1_2_labeled/meta/relative_stats.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
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{}
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Surgical/jhu/imerse/wound_closure/point_labeled/fausto_0_1_jesse_0_1_2_labeled/meta/stats.json
ADDED
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@@ -0,0 +1,242 @@
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|
Surgical/jhu/lcsr/arcade/cholecystectomy/meta/README.md
ADDED
|
@@ -0,0 +1,316 @@
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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 |
+
# Cholecystectomy - README
|
| 8 |
+
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
## 📋 At a Glance
|
| 12 |
+
|
| 13 |
+
*Teleoperated demonstrations of a da Vinci robot performing Cholecystectomy on a pig liver with galblader*
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
---
|
| 17 |
+
|
| 18 |
+
## 📖 Dataset Overview
|
| 19 |
+
|
| 20 |
+
*This dataset contains 750 trajectories of novice surgeons using the dVRK to perform Cholecystectomy. It includes successful actions of grasping and dissecting the gallbladder to provide a robust dataset for training imitation learning policies.*
|
| 21 |
+
|
| 22 |
+
| | |
|
| 23 |
+
| :--- | :--- |
|
| 24 |
+
| **Total Trajectories** | `750` |
|
| 25 |
+
| **Total Hours** | `75` |
|
| 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** | `2.0` |
|
| 29 |
+
|
| 30 |
+
---
|
| 31 |
+
|
| 32 |
+
## 🎯 Tasks & Domain
|
| 33 |
+
|
| 34 |
+
### Domain
|
| 35 |
+
|
| 36 |
+
*Select the primary domain for this dataset.*
|
| 37 |
+
|
| 38 |
+
- [X] **Surgical Robotics**
|
| 39 |
+
- [ ] **Ultrasound Robotics**
|
| 40 |
+
- [ ] **Other Healthcare Robotics** (Please specify: `[Your Domain]`)
|
| 41 |
+
|
| 42 |
+
### Demonstrated Skills
|
| 43 |
+
|
| 44 |
+
*List the primary skills or procedures demonstrated in this dataset.*
|
| 45 |
+
|
| 46 |
+
- Grasping
|
| 47 |
+
- Dissection
|
| 48 |
+
|
| 49 |
+
---
|
| 50 |
+
|
| 51 |
+
## 🔬 Data Collection Details
|
| 52 |
+
|
| 53 |
+
### Collection Method
|
| 54 |
+
|
| 55 |
+
*How was the data collected?*
|
| 56 |
+
|
| 57 |
+
- [X] **Human Teleoperation**
|
| 58 |
+
- [ ] **Programmatic/State-Machine**
|
| 59 |
+
- [ ] **AI Policy / Autonomous**
|
| 60 |
+
- [ ] **Other** (Please specify: `[Your Method]`)
|
| 61 |
+
|
| 62 |
+
### Operator Details
|
| 63 |
+
|
| 64 |
+
| | Description |
|
| 65 |
+
| :--- | :--- |
|
| 66 |
+
| **Operator Count** | `2` |
|
| 67 |
+
| **Operator Skill Level** | `[ ] Expert (e.g., Surgeon, Sonographer)` <br> `[ ] Intermediate (e.g., Trained Researcher)` <br> `[X] Novice (e.g., ML Researcher with minimal experience)` <br> `[ ] N/A` |
|
| 68 |
+
| **Collection Period** | From `[2025-11-3]` to `[2025-12-19]` |
|
| 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 |
+
---
|
| 80 |
+
|
| 81 |
+
## 💡 Diversity Dimensions
|
| 82 |
+
|
| 83 |
+
*Check all dimensions that were intentionally varied during data collection.*
|
| 84 |
+
|
| 85 |
+
- [X] **Camera Position / Angle**
|
| 86 |
+
- [ ] **Lighting Conditions**
|
| 87 |
+
- [ ] **Target Object** (e.g., different phantom models, suture types)
|
| 88 |
+
- [ ] **Spatial Layout** (e.g., placing the target suture needle in various locations)
|
| 89 |
+
- [ ] **Robot Embodiment** (if multiple robots were used)
|
| 90 |
+
- [ ] **Task Execution** (e.g., different techniques for the same task)
|
| 91 |
+
- [ ] **Background / Scene**
|
| 92 |
+
- [ ] **Other** (Please specify: `[Your Dimension]`)
|
| 93 |
+
|
| 94 |
+
*If you checked any of the above please briefly elaborate below.*
|
| 95 |
+
|
| 96 |
+
The camera position was varied per tissue to simulate different angles of approach. This also leads to different views of the tissue and the tools, which can be used to train policies that are robust to different camera angles.
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
---
|
| 100 |
+
|
| 101 |
+
## 🛠️ Equipment & Setup
|
| 102 |
+
|
| 103 |
+
### Robotic Platform(s)
|
| 104 |
+
|
| 105 |
+
- **Robot 1:** `dVRK (da Vinci Research Kit)`
|
| 106 |
+
|
| 107 |
+
### Sensors & Cameras
|
| 108 |
+
|
| 109 |
+
*List the sensors and cameras used. Specify model names where possible. (Add and remove rows as needed)*
|
| 110 |
+
|
| 111 |
+
| Type | Model/Details |
|
| 112 |
+
| :--- | :--- |
|
| 113 |
+
| **Primary Camera** | `Endoscopic Camera, 1920x1080 @ 30fps` |
|
| 114 |
+
| **Wrist Cameras** | `CMOS Endoscopy Camera, 516k Pixel (720 x 720) 1mmx1mm Square Camera, 120 Degree FOV, 2.5 m Length, 6 Pin Connector` |
|
| 115 |
+
|
| 116 |
+
---
|
| 117 |
+
|
| 118 |
+
## 🎯 Action & State Space Representation
|
| 119 |
+
|
| 120 |
+
*Describe how actions and robot states are represented in your dataset. This is crucial for understanding data compatibility and enabling effective policy learning.*
|
| 121 |
+
|
| 122 |
+
### Action Space Representation
|
| 123 |
+
|
| 124 |
+
**Primary Action Representation:**
|
| 125 |
+
- [X] **Absolute Cartesian** (position/orientation relative to robot base)
|
| 126 |
+
- [ ] **Relative Cartesian** (delta position/orientation from current pose)
|
| 127 |
+
- [X] **Joint Space** (direct joint angle commands)
|
| 128 |
+
- [ ] **Other** (Please specify: `[Your Representation]`)
|
| 129 |
+
|
| 130 |
+
**Orientation Representation:**
|
| 131 |
+
- [X] **Quaternions** (x, y, z, w)
|
| 132 |
+
- [ ] **Euler Angles** (roll, pitch, yaw)
|
| 133 |
+
- [ ] **Axis-Angle** (rotation vector)
|
| 134 |
+
- [ ] **Rotation Matrix** (3x3 matrix)
|
| 135 |
+
- [ ] **Other** (Please specify: `[Your Representation]`)
|
| 136 |
+
|
| 137 |
+
**Reference Frame:**
|
| 138 |
+
- [X] **Robot Base Frame**
|
| 139 |
+
- [ ] **Tool/End-Effector Frame**
|
| 140 |
+
- [ ] **World/Global Frame**
|
| 141 |
+
- [ ] **Camera Frame**
|
| 142 |
+
- [ ] **Other** (Please specify: `[Your Frame]`)
|
| 143 |
+
|
| 144 |
+
**Action Dimensions:**
|
| 145 |
+
*List the action space dimensions and their meanings.*
|
| 146 |
+
|
| 147 |
+
```
|
| 148 |
+
action.cartesian_psm1: [x, y, z, qx, qy, qz, qw, jaw]
|
| 149 |
+
- x, y, z: Absolute position in PSM1 base frame (meters)
|
| 150 |
+
- qx, qy, qz, qw: Absolute orientation as quaternion
|
| 151 |
+
- jaw: Jaw/gripper opening angle (radians)
|
| 152 |
+
```
|
| 153 |
+
|
| 154 |
+
```
|
| 155 |
+
action.cartesian_psm2: [x, y, z, qx, qy, qz, qw, jaw]
|
| 156 |
+
- x, y, z: Absolute position in PSM2 base frame (meters)
|
| 157 |
+
- qx, qy, qz, qw: Absolute orientation as quaternion
|
| 158 |
+
- jaw: Jaw/gripper opening angle (radians)
|
| 159 |
+
```
|
| 160 |
+
|
| 161 |
+
```
|
| 162 |
+
action.cartesian_ecm: [x, y, z, qx, qy, qz, qw]
|
| 163 |
+
- x, y, z: Absolute position in ECM base frame (meters)
|
| 164 |
+
- qx, qy, qz, qw: Absolute orientation as quaternion (camera has no gripper)
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
```
|
| 168 |
+
action.joint_psm1: [j1, j2, j3, j4, j5, j6]
|
| 169 |
+
- j1-j3: Joint positions (radians or meters for prismatic joint)
|
| 170 |
+
- j4-j6: Wrist joint positions (radians)
|
| 171 |
+
```
|
| 172 |
+
|
| 173 |
+
```
|
| 174 |
+
action.joint_psm2: [j1, j2, j3, j4, j5, j6]
|
| 175 |
+
- j1-j3: Joint positions (radians or meters for prismatic joint)
|
| 176 |
+
- j4-j6: Wrist joint positions (radians)
|
| 177 |
+
```
|
| 178 |
+
|
| 179 |
+
```
|
| 180 |
+
action.joint_ecm: [j1, j2, j3, j4]
|
| 181 |
+
- j1-j4: Camera manipulator joint positions (radians or meters)
|
| 182 |
+
```
|
| 183 |
+
|
| 184 |
+
```
|
| 185 |
+
Total Action Space (Dual-Arm + ECM):
|
| 186 |
+
- Cartesian: 23 dimensions (8 PSM1 + 8 PSM2 + 7 ECM)
|
| 187 |
+
- Joint Space: 16 dimensions (6 PSM1 + 6 PSM2 + 4 ECM)
|
| 188 |
+
```
|
| 189 |
+
|
| 190 |
+
### State Space Representation
|
| 191 |
+
|
| 192 |
+
**State Information Included:**
|
| 193 |
+
- [X] **Joint Positions** (all articulated joints)
|
| 194 |
+
- [ ] **Joint Velocities**
|
| 195 |
+
- [X] **End-Effector Pose** (Cartesian position/orientation)
|
| 196 |
+
- [ ] **Force/Torque Readings**
|
| 197 |
+
- [X] **Gripper State** (position, force, etc.)
|
| 198 |
+
- [X] **Other** (Please specify: `RCM (Remote Center of Motion) poses for PSM1, PSM2, ECM; SUJ (Setup Joints) poses and joint positions for all arms`)
|
| 199 |
+
|
| 200 |
+
**State Dimensions:**
|
| 201 |
+
|
| 202 |
+
List the state space dimensions and their meanings.
|
| 203 |
+
|
| 204 |
+
observation.state:
|
| 205 |
+
|
| 206 |
+
PSM1 (Patient Side Manipulator 1):
|
| 207 |
+
- psm1_pose: [x, y, z, qx, qy, qz, qw] - End-effector Cartesian pose (meters, quaternion)
|
| 208 |
+
- psm1_sp: [x, y, z, qx, qy, qz, qw] - End-effector setpoint/commanded pose
|
| 209 |
+
- psm1_jaw: Jaw/gripper opening angle (radians)
|
| 210 |
+
- psm1_jaw_sp: Jaw/gripper setpoint angle (radians)
|
| 211 |
+
- psm1_rcm_pose: [x, y, z, qx, qy, qz, qw] - Remote Center of Motion pose
|
| 212 |
+
- psm1_js: [j1, j2, j3, j4, j5, j6] - Joint positions (radians/meters)
|
| 213 |
+
- psm1_set_js: [j1, j2, j3, j4, j5, j6] - Joint setpoints (radians/meters)
|
| 214 |
+
|
| 215 |
+
PSM2 (Patient Side Manipulator 2):
|
| 216 |
+
- psm2_pose: [x, y, z, qx, qy, qz, qw] - End-effector Cartesian pose
|
| 217 |
+
- psm2_sp: [x, y, z, qx, qy, qz, qw] - End-effector setpoint/commanded pose
|
| 218 |
+
- psm2_jaw: Jaw/gripper opening angle (radians)
|
| 219 |
+
- psm2_jaw_sp: Jaw/gripper setpoint angle (radians)
|
| 220 |
+
- psm2_rcm_pose: [x, y, z, qx, qy, qz, qw] - Remote Center of Motion pose
|
| 221 |
+
- psm2_js: [j1, j2, j3, j4, j5, j6] - Joint positions (radians/meters)
|
| 222 |
+
- psm2_set_js: [j1, j2, j3, j4, j5, j6] - Joint setpoints (radians/meters)
|
| 223 |
+
|
| 224 |
+
PSM3 (Patient Side Manipulator 3):
|
| 225 |
+
- psm3_js: [j1, j2, j3, j4, j5, j6] - Joint positions (radians/meters)
|
| 226 |
+
- psm3_set_js: [j1, j2, j3, j4, j5, j6] - Joint setpoints (radians/meters)
|
| 227 |
+
|
| 228 |
+
ECM (Endoscopic Camera Manipulator):
|
| 229 |
+
- ecm_pose: [x, y, z, qx, qy, qz, qw] - Camera end-effector pose
|
| 230 |
+
- ecm_rcm_pose: [x, y, z, qx, qy, qz, qw] - Remote Center of Motion pose
|
| 231 |
+
- ecm_js: [j1, j2, j3, j4] - Joint positions (radians/meters)
|
| 232 |
+
- ecm_set_js: [j1, j2, j3, j4] - Joint setpoints (radians/meters)
|
| 233 |
+
|
| 234 |
+
SUJ (Setup Joints) - Positioning system for each arm:
|
| 235 |
+
- suj1_pose: [x, y, z, qx, qy, qz, qw] - SUJ1 end pose
|
| 236 |
+
- suj1_jp: [j1, j2, j3, j4] - SUJ1 joint positions (radians)
|
| 237 |
+
- suj2_pose: [x, y, z, qx, qy, qz, qw] - SUJ2 end pose
|
| 238 |
+
- suj2_jp: [j1, j2, j3, j4] - SUJ2 joint positions (radians)
|
| 239 |
+
- suj3_pose: [x, y, z, qx, qy, qz, qw] - SUJ3 end pose
|
| 240 |
+
- suj3_jp: [j1, j2, j3, j4] - SUJ3 joint positions (radians)
|
| 241 |
+
- suj_ecm_pose: [x, y, z, qx, qy, qz, qw] - SUJ ECM end pose
|
| 242 |
+
- suj_ecm_jp: [j1, j2, j3, j4] - SUJ ECM joint positions (radians)
|
| 243 |
+
|
| 244 |
+
Total State Dimension: 148 values
|
| 245 |
+
|
| 246 |
+
|
| 247 |
+
### 📋 Recommended Additional Representations
|
| 248 |
+
|
| 249 |
+
*Even if not your primary action/state representation, we strongly encourage including these standardized formats for maximum compatibility:*
|
| 250 |
+
|
| 251 |
+
**Recommended Action Fields:**
|
| 252 |
+
- **`action.cartesian_absolute_psm1`**: Absolute Cartesian pose for PSM1
|
| 253 |
+
```
|
| 254 |
+
[x, y, z, qx, qy, qz, qw, jaw_angle]
|
| 255 |
+
```
|
| 256 |
+
|
| 257 |
+
- **`action.cartesian_absolute_psm2`**: Absolute Cartesian pose for PSM2
|
| 258 |
+
```
|
| 259 |
+
[x, y, z, qx, qy, qz, qw, jaw_angle]
|
| 260 |
+
```
|
| 261 |
+
|
| 262 |
+
- **`action.cartesian_absolute_ecm`**: Absolute Cartesian pose for ECM
|
| 263 |
+
```
|
| 264 |
+
[x, y, z, qx, qy, qz, qw]
|
| 265 |
+
```
|
| 266 |
+
|
| 267 |
+
**Recommended State Fields:**
|
| 268 |
+
- **`bservation.state.joint_positions_psm1`**: Absolute positions for PSM1 joints
|
| 269 |
+
```
|
| 270 |
+
[joint_1, joint_2, joint_3, joint_4, joint_5, joint_6]
|
| 271 |
+
```
|
| 272 |
+
|
| 273 |
+
- **`bservation.state.joint_positions_psm2`**: Absolute positions for PSM2 joints
|
| 274 |
+
```
|
| 275 |
+
[joint_1, joint_2, joint_3, joint_4, joint_5, joint_6]
|
| 276 |
+
```
|
| 277 |
+
|
| 278 |
+
- **`bservation.state.joint_positions_ecm`**: Absolute positions for ECM joints
|
| 279 |
+
```
|
| 280 |
+
[joint_1, joint_2, joint_3, joint_4]
|
| 281 |
+
```
|
| 282 |
+
---
|
| 283 |
+
|
| 284 |
+
|
| 285 |
+
Based on the provided scripts, here's the filled-in documentation for your Data Synchronization Approach:
|
| 286 |
+
|
| 287 |
+
---
|
| 288 |
+
|
| 289 |
+
## ⏱️ Data Synchronization Approach
|
| 290 |
+
|
| 291 |
+
*We capture robot kinematics data and RGB images from multiple camera views (left, right, and two endoscopic cameras), storing timestamps in nanosecond precision directly within image filenames (format: `frame{timestamp_ns}_{camera}.jpg`) and kinematics CSV files. All sensors record timestamps from the same system clock during data collection.*
|
| 292 |
+
|
| 293 |
+
**Synchronization Pipeline:**
|
| 294 |
+
|
| 295 |
+
1. **Image-to-Kinematics Sync**: For each image timestamp extracted from filenames, we find the nearest kinematics data point in the sorted timestamp array. We check both the floor and ceiling indices and select the closest match by absolute time difference.
|
| 296 |
+
|
| 297 |
+
2. **Outlier Filtering**: Frames where the image-to-kinematics time difference exceeds a configurable threshold (default: 30 ms) are marked as outliers and removed from the dataset to ensure temporal alignment quality.
|
| 298 |
+
|
| 299 |
+
3. **Multi-Camera Synchronization**: Using the left camera as the temporal reference, we perform binary search to find matching frames across all camera views. A frame is retained only if **all cameras** have a corresponding image within the synchronization tolerance window. This strict enforcement ensures complete multi-view temporal alignment.
|
| 300 |
+
|
| 301 |
+
4. **Validation and Export**: The filtering pipeline preserves only fully synchronized frames where both camera alignment and kinematics matching criteria are satisfied. Secondary camera frames are renamed to match the left camera's timestamp, maintaining 1:1 correspondence across all modalities.
|
| 302 |
+
|
| 303 |
+
5. **Name and timestamp normalization**: Lastly, we normalize the name of the files to be indexed and the timestamps to be normalized to the start of the episode. This is done by finding the minimum timestamp across all modalities and subtracting it from all timestamps. This ensures that the first frame is always at timestamp 0.
|
| 304 |
+
|
| 305 |
+
---
|
| 306 |
+
|
| 307 |
+
---
|
| 308 |
+
|
| 309 |
+
## 👥 Attribution & Contact
|
| 310 |
+
|
| 311 |
+
| | |
|
| 312 |
+
| :--- | :--- |
|
| 313 |
+
| **Dataset Lead** | `Jacob M. Delgado López` |
|
| 314 |
+
| **Institution** | `Johns Hopkins University` |
|
| 315 |
+
| **Contact Email** | `jdelga16@jh.edu` |
|
| 316 |
+
| **Citation (BibTeX)** | <pre><code>@misc{exvivo_chole_2025,<br> author = {Jacob M. Delgado López, Hao Ding, Lalithkumar Seenivasan, Han Zhang, Antony Goldenberg, Juo-Tung Chen, Xinhao Chen, Idris Sunmola, Mathias Unberath},<br> title = {Ex-Vivo Porcine Cholecystectomy Subtasks for Multimodal VLA Training},<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> |
|
Surgical/jhu/lcsr/arcade/cholecystectomy/meta/episodes.jsonl
ADDED
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| 726 |
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| 732 |
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| 733 |
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| 735 |
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| 736 |
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| 737 |
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| 738 |
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| 739 |
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| 740 |
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| 741 |
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| 743 |
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| 744 |
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| 745 |
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| 746 |
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| 747 |
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| 748 |
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|
| 750 |
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|
Surgical/jhu/lcsr/arcade/cholecystectomy/meta/info.json
ADDED
|
@@ -0,0 +1,226 @@
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