File size: 4,424 Bytes
6a49198 | 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 | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- yam
- manipulation
- bimanual
- imitation-learning
configs:
- config_name: default
data_files: data/**/*.parquet
---
# abc_sort_legos_v21
Teleoperation dataset: **sort the legos into containers by color**
A **LeRobot v2.1** conversion of the `sort_the_legos_into_containers_by_color`
task from [XDOF/ABC-130k](https://huggingface.co/datasets/XDOF/ABC-130k),
prepared for fine-tuning [pi0.5](https://github.com/Physical-Intelligence/openpi)
(`pi05_base`) on the bimanual YAM platform.
## Dataset summary
| Field | Value |
|-------|-------|
| Robot | yam |
| Episodes | 500 (first 500 of 4,458 train episodes, sorted by episode uuid) |
| Total frames | 1,156,733 |
| FPS | 30 Hz |
| Task | sort the legos into containers by color |
| Format | LeRobot v2.1 |
## Is this 30 fps?
Yes — `meta/info.json` reports `fps: 30`, and every camera stream is encoded
at `video.fps: 30`. But note this is a **resampled** 30 fps, not a native
recording rate: in the source ABC-130k MCAP files, the action stream runs at
~200 Hz, state at ~265 Hz, and cameras at 30–60 Hz depending on station type
(each stream on its own independent clock). The conversion builds a fixed
30 Hz tick clock over the overlap window of all streams and does **causal
floor matching** (the latest message at or before each tick) to align
everything onto one common 30 Hz grid — actions are subsampled ~6.7:1, faster
cameras are decimated, and no stream runs below 30 Hz so frames are never
duplicated.
## Cameras
| Name |
|------|
| `head_camera` |
| `left_wrist_camera` |
| `right_wrist_camera` |
Video codec: h264, 640×480 (letterboxed, aspect-ratio preserved). Source
episodes come from two station types — RealSense (mono top camera) and ZED-X
(stereo top camera, one eye picked deterministically per episode) — both
handled by the same conversion.
## State space (`observation.state`, shape `[14]`)
| Index | Name |
|-------|------|
| 0 | `left_joint_0` |
| 1 | `left_joint_1` |
| 2 | `left_joint_2` |
| 3 | `left_joint_3` |
| 4 | `left_joint_4` |
| 5 | `left_joint_5` |
| 6 | `left_gripper` |
| 7 | `right_joint_0` |
| 8 | `right_joint_1` |
| 9 | `right_joint_2` |
| 10 | `right_joint_3` |
| 11 | `right_joint_4` |
| 12 | `right_joint_5` |
| 13 | `right_gripper` |
## Action space (`action`, shape `[14]`)
| Index | Name |
|-------|------|
| 0 | `left_joint_0` |
| 1 | `left_joint_1` |
| 2 | `left_joint_2` |
| 3 | `left_joint_3` |
| 4 | `left_joint_4` |
| 5 | `left_joint_5` |
| 6 | `left_gripper` |
| 7 | `right_joint_0` |
| 8 | `right_joint_1` |
| 9 | `right_joint_2` |
| 10 | `right_joint_3` |
| 11 | `right_joint_4` |
| 12 | `right_joint_5` |
| 13 | `right_gripper` |
State and action are 1:1 index-aligned. Joint values are absolute positions
in **radians**, base → wrist. Gripper is the normalized aperture from
ABC-130k (**0 = closed, 1 = open**). `action` holds the **commanded** joint
positions (source `/{side}-arm-action` + `/{side}-ee-action` topics);
`observation.state` holds the **measured** ones (`/{side}-arm-state` +
`/{side}-ee-state`). Both are absolute, not delta — pi0.5 applies
`DeltaActions` internally at train time.
## How this was converted
Source episodes are MCAP files (`episode.mcap` per episode). Conversion
script:
[`convert_abc_mcap_to_lerobot_v21.py`](https://github.com/Avant-US/openpi) —
`scripts/convert_abc_mcap_to_lerobot_v21.py`. See "Is this 30 fps?" above for
the resampling method.
`meta/episode_ids.json` maps each `episode_index` back to its original
ABC-130k episode uuid for traceability.
## Usage
```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("Sichang0621/abc_sort_legos_v21")
print(ds.num_episodes, ds.num_frames, ds[0]["observation.state"].shape)
```
## Attribution and license
This dataset is **derived from
[XDOF/ABC-130k](https://huggingface.co/datasets/XDOF/ABC-130k)** (Apache-2.0),
released alongside the ABC project ([abc.bot](https://abc.bot/),
[code](https://github.com/amazon-far/abc)). All robot trajectories and
imagery originate from ABC-130k; this repository contributes only the format
conversion described above. Please cite the ABC project when using this data.
Note that the upstream ABC-130k dataset is access-gated on the Hub. Licensed
under Apache-2.0, consistent with the source.
## License
Apache 2.0
|