Datasets:

azithromycin
/

grboguz_test_lfs

license: apache-2.0
authors:
  - grboguz
task_categories:
  - robotics
tags:
  - tsfile
  - timeseries
  - tabular
  - robotics
  - lerobot
  - sentinel_v2
  - manipulation
  - tutorial
modality:
  - timeseries
  - tabular
pretty_name: grboguz test_lfs TsFile
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/grboguz_test_lfs_train.tsfile
size_categories:
  - 10K<n<100K

grboguz/test_lfs TsFile

This dataset is an Apache TsFile conversion of grboguz/test_lfs, a LeRobot v2.1 sentinel_v2 robot-manipulation dataset containing demonstrations for the task "pick the green cube."

Modalities: Time-series, Tabular. The converted repository contains numeric robot states, actions, frame timing, and episode/task tags. The two camera streams remain in the original Hugging Face dataset and are linked below.

Source Dataset and Provenance

Original dataset: grboguz/test_lfs
Pinned source revision: main
Original repository creator and uploader: grboguz
License: Apache-2.0
Robot type: sentinel_v2
LeRobot codebase version: v2.1
Task: pick the green cube (task_index = 0)
Split: train
Sampling rate: 15 fps
Scale: 210 episodes, 72,677 frame rows, 1 task, 210 source Parquet files, 420 source videos
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

The source dataset card provides no paper or completed citation. The Hugging Face repository history attributes the source upload to the grboguz account.

Converted Files

TsFile: data/grboguz_test_lfs_train.tsfile
Table: grboguz_test_lfs_train
Rows: 72,677
Episodes/devices: 210
TsFile size: 1.36 MiB
Time precision: milliseconds
Metadata: meta/ is mirrored from the source, with meta/info.json rewritten to describe the TsFile artifact and conversion mapping.

TsFile Schema

Time is an INT64 millisecond timestamp computed as round(timestamp * 1000) and restarts for each episode.

TAG columns (stored as TsFile STRING tags while preserving the original source dtype in conversion metadata):

episode_index
task_index

FIELD columns:

frame_index
sample_index
observation_state_0
observation_state_1
observation_state_2
action_0
action_1
action_2
action_3

Flattened vector groups:

observation.state -> observation_state_0 ... observation_state_2 (3 FLOAT fields)
action -> action_0 ... action_3 (4 FLOAT fields)

Conversion Notes

The shared config-driven lerobot converter is used; the included convert_grboguz_test_lfs.py is the dataset-specific local entry point.
The train split is merged into one table-model TsFile. Filter by episode_index and task_index to select an episode or task.
Storage profile: Time uses TS_2DIFF + LZ4; FLOAT/DOUBLE use GORILLA + LZ4; INT32/INT64 use TS_2DIFF + LZ4; BOOLEAN uses RLE + LZ4. The episode_index and task_index columns remain TsFile table-model TAG/device columns.
action[4] and observation.state[3] are flattened to scalar FLOAT fields; the full source prefix is retained and . is replaced with _.
The source timestamp column is dropped after Time synthesis because it is redundant with Time / 1000 seconds.
The source index column is retained as sample_index; frame_index is retained unchanged.
All 72,677 source rows and all 7 action/state dimensions are retained.

Videos

Videos are not duplicated in this converted repository. The source contains two frame-aligned camera streams, each with 210 per-episode MP4 files:

observation.image - 210 per-episode MP4 files
observation.wrist_image - 210 per-episode MP4 files

The numeric TsFile rows remain aligned with the original videos through episode_index, frame_index, and the source per-episode metadata.

Validation

The generated TsFile is checked for successful tool completion, non-zero size, table schema, metadata row-count equality with the staged Parquet, and a query readback sample. See VALIDATION.md and validation_report.json.

Minimal Read Example

from tsfile import TsFileReader

reader = TsFileReader("data/grboguz_test_lfs_train.tsfile")
table_name = "grboguz_test_lfs_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())
reader.close()

Citation

The source dataset card provides no paper or completed citation. Cite the original Hugging Face dataset and the grboguz account when using this converted artifact.