Search is not available for this dataset
action listlengths 20 20 | observation.state listlengths 20 20 | joint_state listlengths 14 14 | joint_action listlengths 14 14 | is_demo bool 1
class | is_intervention bool 1
class | timestamp float32 0 381 | frame_index int64 0 11.4k | episode_index int64 0 3.82k | index int64 0 13.3M | task_index int64 0 5 |
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End of preview. Expand in Data Studio
ABC-130k — 256² full-frame re-conversion (train split)
Full-resolution re-conversion of the ABC-130k dual-YAM bimanual manipulation corpus (XDOF/ABC-130k raw recordings, Apache-2.0) in LeRobot v3.0 format.
This split: 129,032 episodes · 381,742,382 frames · 197 tasks · 30 fps. Validation split: huzheyuan/abc130k_v3_val (1,592 episodes / 189 tasks, task strings aligned to this split).
Why another port
The existing LeRobot port (lerobot/abc_130k_v3_train) letterboxes the 16:10 / 4:3 source streams into 224×224, so only 224×140 (stereo stations) or 224×168 (RealSense stations) of each frame is active — 30–40 % of every image is black bars. This conversion decodes the original camera streams (1920×1200 H.265 stereo stations, 640×480 H.264 RealSense stations) and squash-resizes the full frame to 256×256 (SVT-AV1, CRF 30): no letterboxing, ~30 % more active pixels per axis.
Lowdim data was converted from the same raw streams and validated against the LeRobot port tick-for-tick (episode alignment count-exact; full-trajectory joint max |Δ| = 1.2e-7 rad on both station types).
Differences vs the lerobot port
- 4 tasks absent (193 episodes):
mix the coffee capsules…,dust the bookshelf…,put the gloves into the storage basket,tie the trash bag— these were removed from the raw corpus after the port was made, so no source video exists for them. - Frames are full-image 256×256 (squashed), not 224×224 letterboxed.
- Extra features:
joint_state/joint_action(14-dim raw joint layout) alongside the 20-dim Cartesianobservation.state/action.
Features
| key | shape | notes |
|---|---|---|
observation.images.top |
256×256×3 @30 fps | AV1; stereo stations: one eye of the top stereo pair, picked deterministically per episode (same export rule as the lerobot port) |
observation.images.left_wrist |
256×256×3 @30 fps | AV1 |
observation.images.right_wrist |
256×256×3 @30 fps | AV1 |
observation.state |
(20,) float32 | 2 arms × [xyz(3), 6D rotation(6), gripper(1)] |
action |
(20,) float32 | same layout; true commanded targets |
joint_state |
(14,) float32 | [l_joints(6), l_grip, r_joints(6), r_grip], radians |
joint_action |
(14,) float32 | same layout, commanded |
Conventions: Cartesian pose is forward kinematics at the wrist flange (the corpus mixes three gripper hardware types with no per-episode label, so no TCP offset is applied); rotation is 6D (first two rotation-matrix columns); canonical +z = approach axis, y = jaw axis; per-arm base frames (REP-103, +x task-forward); grippers are fraction-open in [0, 1] (1 = open) in all four vectors.
Usage
from lerobot.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("huzheyuan/abc130k_v3_train")
item = ds[0] # dict with images (CHW float32), state, action, ...
Loading note: timestamp tolerance
LeRobotDataset builds its video-timestamp queries in float32. Past ~1000 s into a
packed v3 video file this misses the exact 30 fps grid by ~1e-4 s, which equals the
default tolerance_s = 1e-4 and raises FrameTimestampError on perfectly valid
data (the frame grid itself is exact; this affects every large packed v3 dataset,
not just this one). Work around it by passing a slightly larger tolerance:
ds = LeRobotDataset("huzheyuan/abc130k_v3_train", tolerance_s=1e-3)
License
Apache-2.0, same as the source corpus. If you use this dataset, please also credit the original ABC-130k release (XDOF).
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