huzheyuan/abc130k_v3_train

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 Cartesian observation.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, ...

License

Apache-2.0, same as the source corpus. If you use this dataset, please also credit the original ABC-130k release (XDOF).