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README.md
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| 1 |
+
---
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| 2 |
+
license: apache-2.0
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| 3 |
+
task_categories:
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| 4 |
+
- robotics
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| 5 |
+
tags:
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| 6 |
+
- LeRobot
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| 7 |
+
- yam
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| 8 |
+
- manipulation
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| 9 |
+
- bimanual
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| 10 |
+
- imitation-learning
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| 11 |
+
configs:
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| 12 |
+
- config_name: default
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| 13 |
+
data_files: data/**/*.parquet
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| 14 |
+
---
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| 15 |
+
|
| 16 |
+
# abc_sort_legos_v21
|
| 17 |
+
|
| 18 |
+
Teleoperation dataset: **sort the legos into containers by color**
|
| 19 |
+
|
| 20 |
+
A **LeRobot v2.1** conversion of the `sort_the_legos_into_containers_by_color`
|
| 21 |
+
task from [XDOF/ABC-130k](https://huggingface.co/datasets/XDOF/ABC-130k),
|
| 22 |
+
prepared for fine-tuning [pi0.5](https://github.com/Physical-Intelligence/openpi)
|
| 23 |
+
(`pi05_base`) on the bimanual YAM platform.
|
| 24 |
+
|
| 25 |
+
## Dataset summary
|
| 26 |
+
|
| 27 |
+
| Field | Value |
|
| 28 |
+
|-------|-------|
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| 29 |
+
| Robot | yam |
|
| 30 |
+
| Episodes | 500 (first 500 of 4,458 train episodes, sorted by episode uuid) |
|
| 31 |
+
| Total frames | 1,156,733 |
|
| 32 |
+
| FPS | 30 Hz |
|
| 33 |
+
| Task | sort the legos into containers by color |
|
| 34 |
+
| Format | LeRobot v2.1 |
|
| 35 |
+
|
| 36 |
+
## Is this 30 fps?
|
| 37 |
+
|
| 38 |
+
Yes — `meta/info.json` reports `fps: 30`, and every camera stream is encoded
|
| 39 |
+
at `video.fps: 30`. But note this is a **resampled** 30 fps, not a native
|
| 40 |
+
recording rate: in the source ABC-130k MCAP files, the action stream runs at
|
| 41 |
+
~200 Hz, state at ~265 Hz, and cameras at 30–60 Hz depending on station type
|
| 42 |
+
(each stream on its own independent clock). The conversion builds a fixed
|
| 43 |
+
30 Hz tick clock over the overlap window of all streams and does **causal
|
| 44 |
+
floor matching** (the latest message at or before each tick) to align
|
| 45 |
+
everything onto one common 30 Hz grid — actions are subsampled ~6.7:1, faster
|
| 46 |
+
cameras are decimated, and no stream runs below 30 Hz so frames are never
|
| 47 |
+
duplicated.
|
| 48 |
+
|
| 49 |
+
## Cameras
|
| 50 |
+
|
| 51 |
+
| Name |
|
| 52 |
+
|------|
|
| 53 |
+
| `head_camera` |
|
| 54 |
+
| `left_wrist_camera` |
|
| 55 |
+
| `right_wrist_camera` |
|
| 56 |
+
|
| 57 |
+
Video codec: h264, 640×480 (letterboxed, aspect-ratio preserved). Source
|
| 58 |
+
episodes come from two station types — RealSense (mono top camera) and ZED-X
|
| 59 |
+
(stereo top camera, one eye picked deterministically per episode) — both
|
| 60 |
+
handled by the same conversion.
|
| 61 |
+
|
| 62 |
+
## State space (`observation.state`, shape `[14]`)
|
| 63 |
+
|
| 64 |
+
| Index | Name |
|
| 65 |
+
|-------|------|
|
| 66 |
+
| 0 | `left_joint_0` |
|
| 67 |
+
| 1 | `left_joint_1` |
|
| 68 |
+
| 2 | `left_joint_2` |
|
| 69 |
+
| 3 | `left_joint_3` |
|
| 70 |
+
| 4 | `left_joint_4` |
|
| 71 |
+
| 5 | `left_joint_5` |
|
| 72 |
+
| 6 | `left_gripper` |
|
| 73 |
+
| 7 | `right_joint_0` |
|
| 74 |
+
| 8 | `right_joint_1` |
|
| 75 |
+
| 9 | `right_joint_2` |
|
| 76 |
+
| 10 | `right_joint_3` |
|
| 77 |
+
| 11 | `right_joint_4` |
|
| 78 |
+
| 12 | `right_joint_5` |
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| 79 |
+
| 13 | `right_gripper` |
|
| 80 |
+
|
| 81 |
+
## Action space (`action`, shape `[14]`)
|
| 82 |
+
|
| 83 |
+
| Index | Name |
|
| 84 |
+
|-------|------|
|
| 85 |
+
| 0 | `left_joint_0` |
|
| 86 |
+
| 1 | `left_joint_1` |
|
| 87 |
+
| 2 | `left_joint_2` |
|
| 88 |
+
| 3 | `left_joint_3` |
|
| 89 |
+
| 4 | `left_joint_4` |
|
| 90 |
+
| 5 | `left_joint_5` |
|
| 91 |
+
| 6 | `left_gripper` |
|
| 92 |
+
| 7 | `right_joint_0` |
|
| 93 |
+
| 8 | `right_joint_1` |
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| 94 |
+
| 9 | `right_joint_2` |
|
| 95 |
+
| 10 | `right_joint_3` |
|
| 96 |
+
| 11 | `right_joint_4` |
|
| 97 |
+
| 12 | `right_joint_5` |
|
| 98 |
+
| 13 | `right_gripper` |
|
| 99 |
+
|
| 100 |
+
State and action are 1:1 index-aligned. Joint values are absolute positions
|
| 101 |
+
in **radians**, base → wrist. Gripper is the normalized aperture from
|
| 102 |
+
ABC-130k (**0 = closed, 1 = open**). `action` holds the **commanded** joint
|
| 103 |
+
positions (source `/{side}-arm-action` + `/{side}-ee-action` topics);
|
| 104 |
+
`observation.state` holds the **measured** ones (`/{side}-arm-state` +
|
| 105 |
+
`/{side}-ee-state`). Both are absolute, not delta — pi0.5 applies
|
| 106 |
+
`DeltaActions` internally at train time.
|
| 107 |
+
|
| 108 |
+
## How this was converted
|
| 109 |
+
|
| 110 |
+
Source episodes are MCAP files (`episode.mcap` per episode). Conversion
|
| 111 |
+
script:
|
| 112 |
+
[`convert_abc_mcap_to_lerobot_v21.py`](https://github.com/Avant-US/openpi) —
|
| 113 |
+
`scripts/convert_abc_mcap_to_lerobot_v21.py`. See "Is this 30 fps?" above for
|
| 114 |
+
the resampling method.
|
| 115 |
+
|
| 116 |
+
`meta/episode_ids.json` maps each `episode_index` back to its original
|
| 117 |
+
ABC-130k episode uuid for traceability.
|
| 118 |
+
|
| 119 |
+
## Usage
|
| 120 |
+
|
| 121 |
+
```python
|
| 122 |
+
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
|
| 123 |
+
|
| 124 |
+
ds = LeRobotDataset("Sichang0621/abc_sort_legos_v21")
|
| 125 |
+
print(ds.num_episodes, ds.num_frames, ds[0]["observation.state"].shape)
|
| 126 |
+
```
|
| 127 |
+
|
| 128 |
+
## Attribution and license
|
| 129 |
+
|
| 130 |
+
This dataset is **derived from
|
| 131 |
+
[XDOF/ABC-130k](https://huggingface.co/datasets/XDOF/ABC-130k)** (Apache-2.0),
|
| 132 |
+
released alongside the ABC project ([abc.bot](https://abc.bot/),
|
| 133 |
+
[code](https://github.com/amazon-far/abc)). All robot trajectories and
|
| 134 |
+
imagery originate from ABC-130k; this repository contributes only the format
|
| 135 |
+
conversion described above. Please cite the ABC project when using this data.
|
| 136 |
+
|
| 137 |
+
Note that the upstream ABC-130k dataset is access-gated on the Hub. Licensed
|
| 138 |
+
under Apache-2.0, consistent with the source.
|
| 139 |
+
|
| 140 |
+
## License
|
| 141 |
+
|
| 142 |
+
Apache 2.0
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meta/episodes.jsonl
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meta/episodes_stats.jsonl
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meta/info.json
ADDED
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meta/stats.json
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@@ -0,0 +1,140 @@
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+
1156733
|
| 138 |
+
]
|
| 139 |
+
}
|
| 140 |
+
}
|
meta/tasks.jsonl
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"task_index": 0, "task": "sort the legos into containers by color"}
|