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,
prepared for fine-tuning pi0.5
(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 —
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
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 (Apache-2.0), released alongside the ABC project (abc.bot, code). 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