Datasets:

azithromycin
/

120ft_first-thread-gr00t

authors:
  - 120ft Factory AB
  - chrisvtom
task_categories:
  - robotics
tags:
  - tsfile
  - timeseries
  - tabular
  - robotics
  - lerobot
  - gr00t
  - unitree-g1
modality:
  - timeseries
  - tabular
pretty_name: First Thread GR00T TsFile
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/120ft_first_thread_gr00t_train.tsfile
size_categories:
  - 10K<n<100K

First Thread GR00T TsFile

This dataset is an Apache TsFile conversion of 120ft/first-thread-gr00t, a LeRobot v2.1 / GR00T Unitree G1-D teleoperation dataset for the task "thread lamp part".

The converted repository contains time-series and tabular numeric robot data. The three camera streams remain in the original Hugging Face dataset and are not duplicated here.

Source Dataset and Provenance

Original dataset: 120ft/first-thread-gr00t
Pinned source revision: b8a57a601ac099cec83fe0e49589e1032f2d197c
Publisher/original organization: 120ft Factory AB (120ft)
Repository and video contributor: chrisvtom
License: not declared by the source dataset card
Paper/citation: not provided by the source dataset card
Robot type: g1d
LeRobot codebase version: v2.1
Task: thread lamp part (task_index=0)
Split: train
Sampling rate: 30 fps
Scale: 72 episodes, 96,960 frames, 1 task
Source Parquet layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
Source Parquet shards: 72

The source card states that the v2.1 GR00T form was converted from a v3.0 dataset in the 120ft data-collection repository. This conversion uses the pinned published LeRobot v2.1 Parquet files as its authoritative numeric input.

Converted Files

TsFile: data/120ft_first_thread_gr00t_train.tsfile
Table: 120ft_first_thread_gr00t_train
Rows: 96,960
Episodes/devices: 72
Columns: 37 total (1 TIME, 2 TAG, 34 FIELD)
TsFile size: 6.30 MiB
Time precision: milliseconds
Metadata: source meta/ files are preserved; meta/info.json is rewritten to describe the TsFile schema, mapping, provenance, and video policy.

TsFile Schema

Time is an INT64 millisecond timestamp computed as round(timestamp * 1000). It restarts at zero in each episode.

Role	Columns	TsFile type
TIME	`Time`	INT64/TIMESTAMP
TAG	`episode_index`, `task_index`	STRING device tags
FIELD	`frame_index`, `sample_index`	INT64
FIELD	`observation_state_0` ... `observation_state_15`	FLOAT
FIELD	`action_0` ... `action_15`	FLOAT

The 16 state/action dimensions retain the ordering documented in source meta/info.json: seven left-arm joints, seven right-arm joints, then left and right Dex1 gripper values.

Conversion Notes

All 72 source episode Parquet files are merged into one train table-model TsFile. Filter by episode_index and task_index to select a trajectory.
observation.state[16] and action[16] are flattened to scalar FLOAT fields. Source prefixes are preserved and . is replaced with _.
Source timestamp is dropped after Time synthesis because it is exactly represented by Time / 1000 seconds at millisecond precision.
Source index is retained as sample_index; frame_index is unchanged.
No source rows, episodes, tasks, or numeric vector dimensions are dropped.

Encoding and Compression

The conversion generated and fully validated both permitted numeric profiles, then selected the smaller valid result (zstd):

Time: TS_2DIFF + LZ4
FLOAT/DOUBLE FIELD values: GORILLA + ZSTD
INT32/INT64 FIELD values: TS_2DIFF + ZSTD
BOOLEAN FIELD values: RLE + LZ4
TAG values: TsFile table device/TAG mechanism (PLAIN + LZ4 storage)

The final TsFile is 0.5578 times the combined size of the 72 source Parquet files.

Videos

Videos are not included in this converted repository. The pinned source has 216 frame-aligned H.264 480x640 MP4 files at 30 fps, split across three streams:

observation.images.head - 72 per-episode MP4 files
observation.images.left_wrist - 72 per-episode MP4 files
observation.images.right_wrist - 72 per-episode MP4 files

Source template: videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4. Numeric rows remain aligned with the original videos through episode_index, frame_index, and source episode metadata.

Validation

Local validation checks source and staged row counts, unique (episode_index, task_index, Time) keys, per-episode monotonic Time, table/TAG schema, full Java query readback, SHA-256, and actual per-column encodings and compression. Conversion scripts and validation reports are intentionally not part of the upload-ready dataset.

Minimal Read Example

from tsfile import TsFileReader

reader = TsFileReader("data/120ft_first_thread_gr00t_train.tsfile")
table_name = "120ft_first_thread_gr00t_train"
columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "observation_state_0",
    "action_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 does not provide a paper or completed citation. Cite the original Hugging Face dataset, 120ft Factory AB, and the pinned source revision when using this converted artifact.