azithromycin/axiboai_piper_stacking_ee

Piper Stacking EE TsFile

This dataset is an Apache TsFile conversion of axiboai/piper_stacking_ee, a LeRobot v2.1 bimanual robot-manipulation dataset for stacking a red cube on a blue cube.

Modalities: Time-series. The converted repository contains numeric robot observations, actions, frame timing, episode/task tags, and mirrored source metadata. All three camera video streams remain in the original Hugging Face dataset.

Source Dataset and Author

• Source dataset: axiboai/piper_stacking_ee
• Pinned source revision: 7f79b7c26d815766cf0f6d041edde20e7033e573
• Publishing organization: AXIBO (axiboai)
• Source uploader/contributor: Ishan Shah (Ishan-Axibo)
• Authorship evidence: the source repository's file history attributes the uploaded dataset tree to Ishan-Axibo; the source card provides no separate formal author list.
• License: Apache-2.0
• Robot type: piperx_bimanual_eef_6d
• LeRobot codebase version: v2.1
• Task: Stack the red cube on the blue cube.
• Split: train
• Scale: 155 episodes, 60,656 frames, 1 task, 30 fps
• Episode lengths: 57 to 707 frames (mean 391.33)
• Source frame files: 155 Parquet files under data/chunk-000/
• Source frame layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
• Source video layout: videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4
• Paper/citation: not supplied by the source dataset card

Converted Files

• TsFile: data/piper_stacking_ee_train.tsfile
• Table: piper_stacking_ee_train
• Rows: 60,656
• Episodes/devices: 155
• Tasks: 1
• Time precision: milliseconds
• Metadata: meta/ is mirrored from the source, with meta/info.json rewritten to describe the TsFile artifact and video policy.

TsFile Schema

Time is synthesized as round(timestamp * 1000) milliseconds and restarts from 0 for each episode.

TAG columns:

• episode_index
• task_index

Scalar FIELD columns:

• frame_index
• sample_index, renamed from source column index

Flattened FLOAT FIELD groups:

• observation.state[20] -> observation_state_0 ... observation_state_19
• action[20] -> action_0 ... action_19

The source uses the same 20-dimensional order for state and action:

Index	Source dimension	State FIELD	Action FIELD
0	left_x	observation_state_0	action_0
1	left_y	observation_state_1	action_1
2	left_z	observation_state_2	action_2
3	left_rot6d_0	observation_state_3	action_3
4	left_rot6d_1	observation_state_4	action_4
5	left_rot6d_2	observation_state_5	action_5
6	left_rot6d_3	observation_state_6	action_6
7	left_rot6d_4	observation_state_7	action_7
8	left_rot6d_5	observation_state_8	action_8
9	left_gripper	observation_state_9	action_9
10	right_x	observation_state_10	action_10
11	right_y	observation_state_11	action_11
12	right_z	observation_state_12	action_12
13	right_rot6d_0	observation_state_13	action_13
14	right_rot6d_1	observation_state_14	action_14
15	right_rot6d_2	observation_state_15	action_15
16	right_rot6d_3	observation_state_16	action_16
17	right_rot6d_4	observation_state_17	action_17
18	right_rot6d_5	observation_state_18	action_18
19	right_gripper	observation_state_19	action_19

Conversion Notes

• The shared config-driven lerobot converter is used; the dataset-specific script is a thin local orchestration and validation entry point and is not part of the dataset upload; the conversion script is retained locally.
• The train split is merged into one table-model TsFile. Filter by episode_index and task_index to select an episode or task.
• Vector columns are flattened to scalar TsFile fields. Full source prefixes are preserved, with . replaced by _.
• The source timestamp column is dropped after Time synthesis because it is redundant with Time / 1000 seconds.
• Source index is renamed to sample_index; frame_index is retained.
• All 155 source Parquet schemas are identical, both vector widths are exactly 20, and no numeric row, episode, task, state dimension, or action dimension is dropped.
• FLOAT fields use GORILLA + LZ4; INT64 fields and Time use TS_2DIFF + LZ4. The config also requires RLE + LZ4 for BOOLEAN fields (none occur in this source).

Videos

Videos are not duplicated in this converted repository. They remain under the original source revision's videos/chunk-000/ tree in three frame-aligned streams:

• observation.images.cam_front: 155 MP4 files, 652,759,687 bytes
• observation.images.cam_left_wrist: 155 MP4 files, 585,180,346 bytes
• observation.images.cam_right_wrist: 155 MP4 files, 459,890,635 bytes

Together, the source contains 465 MP4 files totaling 1,697,830,668 bytes (about 1.58 GiB). Each stream contains one episode_XXXXXX.mp4 file per episode. Numeric rows remain aligned through episode_index, frame_index, and the source episode metadata. The source metadata describes 640x480 AV1 video at 30 fps without audio.

Validation

The generated TsFile was opened and fully queried with the Apache TsFile Java reader bundled with the conversion tools. Source, staged-Parquet, and TsFile readback row counts all equal 60,656. The file contains one table, 155 episode/task devices, 2 TAG columns, and 42 FIELD columns.

The detailed VALIDATION.md and validation_report.json checks are retained locally and are intentionally not included in the dataset upload.

Usage

from tsfile import TsFileReader

reader = TsFileReader("data/piper_stacking_ee_train.tsfile")
table_name = "piper_stacking_ee_train"
columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "action_0",
    "observation_state_0",
]

with reader.query_table(table_name, columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())