Datasets:
File size: 6,891 Bytes
2259d70 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | # UniHand Human Data Preview (Customized LeRobot v3 Format)
This is a preview of the human data of UniHand in the customized LeRobot v3 format.
We customized the LeRobot v3 format to better fit the structure of our human data and to facilitate downstream usage. The dataset layout and file formats are designed to be intuitive and efficient for common use cases.
## 1. Dataset Layout
```text
{dataset_root}/
data/
chunk-000/file-000000.parquet
chunk-000/file-000001.parquet
...
videos/
ego-view/
chunk-000/file-000000.mp4
chunk-000/file-000001.mp4
...
meta/
info.json
tasks_instruction.jsonl
tasks_description.jsonl
episodes/
instruction/
chunk-000/file-000000.parquet
...
description/
chunk-000/file-000000.parquet
...
```
## 2. `meta/info.json`
`meta/info.json` stores dataset-level metadata.
Example:
```json
{
"base_dataset": "arctic",
"dataset_variants": ["arctic", "arctic_aug", "arctic_aug2"],
"fps": 30.0,
"action_stride": 1,
"robot_type": "human hand",
"layout": "file-centric-split-mirror-v4",
"mirror_video_transform": "horizontal_flip",
"mirror_text_transform_version": "left-right-clockwise-v1",
"num_files": 1641,
"num_episodes": 229814
}
```
Common keys:
- `base_dataset`
- `dataset_variants`
- `fps`
- `action_stride`
- `robot_type`
- `layout`
- `mirror_video_transform`
- `mirror_text_transform_version`
- `num_files`
- `num_episodes`
How to use it:
- read `layout` as the dataset layout identifier
- read `action_stride` to interpret action horizons
- read `fps` as the default frame rate if you need dataset-level timing metadata
## 3. `meta/tasks_instruction.jsonl`
This file is the text registry for instruction / prediction samples.
Each line is one JSON object:
```json
{"task_index": 0, "task": "Pick up the object ..."}
```
Fields:
- `task_index`: integer task id
- `task`: task text
How to use it:
- if you are reading from `meta/episodes/instruction/...`, use this file to resolve `task_index`
- `task_index` is contiguous and starts from `0` within this file
## 4. `meta/tasks_description.jsonl`
This file is the text registry for description samples.
Each line is one JSON object:
```json
{"task_index": 0, "task": "The hands lift the object ..."}
```
Fields:
- `task_index`: integer task id
- `task`: text description
How to use it:
- if you are reading from `meta/episodes/description/...`, use this file to resolve `task_index`
- `task_index` is contiguous and starts from `0` within this file
## 5. `meta/episodes/instruction/**/*.parquet`
These parquet shards store instruction / prediction episode rows.
Each row describes one temporal slice inside one exported file.
Common fields:
- `file_id`
- `start_timestep`
- `end_timestep`
- `embodiment`
- `task_index`
- `row_id`
Meaning:
- `file_id`: file-level id used to locate motion parquet and video
- `start_timestep`: inclusive start frame
- `end_timestep`: exclusive end frame
- `embodiment`: effective embodiment for this row
- `task_index`: text id in `meta/tasks_instruction.jsonl`
- `row_id`: stable row id
How to use it:
- read one row
- resolve text from `meta/tasks_instruction.jsonl`
- resolve motion/video from `file_id`
- slice the file timeline with `[start_timestep, end_timestep)`
- each parquet shard contains many episode rows, and those rows may reference many different `file_id` values
## 6. `meta/episodes/description/**/*.parquet`
These parquet shards store description episode rows.
The row structure is the same as the instruction split:
- `file_id`
- `start_timestep`
- `end_timestep`
- `embodiment`
- `task_index`
- `row_id`
How to use it:
- read one row
- resolve text from `meta/tasks_description.jsonl`
- resolve motion/video from `file_id`
- slice the file timeline with `[start_timestep, end_timestep)`
- each parquet shard contains many episode rows, and those rows may reference many different `file_id` values
## 7. `data/chunk-xxx/file-xxxxxx.parquet`
Each motion parquet stores frame-level hand motion for one `file_id`.
Common per-frame columns:
- `camera_c2w`
- `left.trans_w`
- `left.rot_axis_angle_w`
- `left.theta`
- `left.beta`
- `right.trans_w`
- `right.rot_axis_angle_w`
- `right.theta`
- `right.beta`
- `valid.left_horizon`
- `valid.right_horizon`
- `valid.joint_horizon`
Meaning:
- `camera_c2w`: flattened camera-to-world transform
- `left/right.trans_w`: wrist translation in world frame
- `left/right.rot_axis_angle_w`: wrist rotation in axis-angle form
- `left/right.theta`: MANO pose parameters
- `left/right.beta`: MANO shape parameters
- `valid.*_horizon`: future validity horizon for action extraction
How to use it:
- locate the file from `file_id`
- load the parquet
- use frame indices in the global file timeline
- if an episode row is `(start_timestep, end_timestep)`, only use frames in that interval
Path rule:
```text
data/chunk-{file_id // 1000:03d}/file-{file_id:06d}.parquet
```
## 8. `videos/ego-view/chunk-xxx/file-xxxxxx.mp4`
These mp4 files store the ego-view video aligned with the motion parquet.
How to resolve the path:
1. Read `file_id`.
2. Use:
```text
videos/ego-view/chunk-{file_id // 1000:03d}/file-{file_id:06d}.mp4
```
## 9. How To Read One Sample
### Instruction / prediction sample
1. Read one row from `meta/episodes/instruction/**/*.parquet`.
2. Use `task_index` to look up text in `meta/tasks_instruction.jsonl`.
3. Use `file_id` to locate the motion parquet in `data/...`.
4. Use `file_id` to locate the ego-view video in `videos/ego-view/...`.
5. Restrict the valid episode range to `start_timestep <= t < end_timestep`.
6. Read the frame(s) you need from the motion parquet.
7. Read the aligned video frame(s) using the same file-global frame index.
### Description sample
1. Read one row from `meta/episodes/description/**/*.parquet`.
2. Use `task_index` to look up text in `meta/tasks_description.jsonl`.
3. Use `file_id` to locate the motion parquet in `data/...`.
4. Use `file_id` to locate the ego-view video in `videos/ego-view/...`.
5. Restrict the valid episode range to `start_timestep <= t < end_timestep`.
6. Read the frame(s) you need from the motion parquet.
7. Read the aligned video frame(s) using the same file-global frame index.
## 10. Valid Horizon
The `valid.*_horizon` columns are used to check whether a timestep can support future action extraction.
Use:
- `valid.left_horizon` for left-hand rows
- `valid.right_horizon` for right-hand rows
- `valid.joint_horizon` for bimanual rows
The horizon is measured in units of `meta/info.json["action_stride"]`.
If your base timestep is `t` and your future chunk needs `K` stride-steps, require:
```text
selected_horizon[t] >= K
```
and also require all sampled future timesteps to stay inside the episode range:
```text
t + K * action_stride < end_timestep
```
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