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Datasets:
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axiboai_piper_stacking_ee

Tasks:
Robotics
Modalities:
Time-series
Tabular
Size:
10K<n<100K
Tags:
tsfile
timeseries
tabular
robotics
lerobot
manipulation
License:
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metadata 
license: apache-2.0
authors:
  - Ishan Shah (Ishan-Axibo)
task_categories:
  - robotics
tags:
  - tsfile
  - timeseries
  - tabular
  - robotics
  - lerobot
  - manipulation
  - bimanual
  - cube-stacking
modality:
  - timeseries
  - tabular
pretty_name: Piper Stacking EE TsFile
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/piper_stacking_ee_train.tsfile
size_categories:
  - 10K<n<100K

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:

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())