Hugging Face's logo

Datasets:
azithromycin
/
unitreerobotics_G1_Dex1_Clean_Table

Tasks:
Robotics
Modalities:
Time-series
Tabular
Size:
100K<n<1M
Tags:
tsfile
timeseries
tabular
License:
Dataset card Files
xet
 Community
unitreerobotics_G1_Dex1_Clean_Table / README.md
azithromycin's picture
azithromycin
Add TsFile (converted from unitreerobotics/G1_Dex1_Clean_Table)
b865763 verified
preview code
|
Raw
History Blame Contribute Delete
4.72 kB
metadata 
license: apache-2.0
task_categories:
  - robotics
tags:
  - tsfile
  - timeseries
  - tabular
modality:
  - timeseries
  - tabular
  - video
pretty_name: G1 Dex1 Clean Table TsFile
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/unitreerobotics_G1_Dex1_Clean_Table.tsfile
size_categories:
  - 100K<n<1M

G1 Dex1 Clean Table — Apache TsFile conversion

This repository is a compact Apache TsFile representation of the unitreerobotics/G1_Dex1_Clean_Table LeRobot dataset. It contains the robot state/action time series and episode/task metadata. The source camera videos are intentionally not copied here.

Source dataset and attribution

  • Publisher/authors: Unitree Robotics; dataset contributors shown by Hugging Face are wangcong and wangcong627.
  • License: Apache-2.0.
  • Homepage: UnifoLM-VLA-0.
  • Task: organize and tidy items on a table (7-DOF dual-arm G1, gripper end effectors).
  • Acquisition: 30 Hz; 640×480 images; approximately 20–40 seconds per operation.
  • Citation/paper: the original dataset card does not provide a BibTeX citation or paper reference.

The source has one train split with 200 episodes, 265,701 rows and one task. There are 200 source Parquet episode files in data/chunk-000/ and 800 source video files (four streams × 200 episodes). The original video layout is:

videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4

For this dataset, video_key is one of observation.images.cam_left_high, cam_right_high, cam_left_wrist, or cam_right_wrist. See the original videos directory. Videos remain in the original Hugging Face dataset and are not included in this TsFile repository; align them to rows with episode_index and frame_index.

Converted artifact

  • TsFile: data/unitreerobotics_G1_Dex1_Clean_Table.tsfile
  • Table: unitreerobotics_G1_Dex1_Clean_Table (the Python SDK exposes the normalized lower-case table name)
  • Rows: 265,701
  • Episodes/devices: 200
  • Time precision: integer milliseconds
  • Source Parquet shards: 200 episode files, merged into one TsFile

Schema

Category Columns
TIME Time (INT64, ms)
TAG/device episode_index (INT64), task_index (INT64)
FIELD scalars frame_index, sample_index (INT64); four gripper scalars (FLOAT)
FIELD vectors observation.left_arm/right_arm (7 each), observation.left_ee/right_ee (6 each), observation.body (29), and matching action.* vectors, flattened to scalar *_0 … *_{N-1} FLOAT fields

Dots in source names are replaced by underscores while preserving the full prefix (for example, observation.left_arm → observation_left_arm_0..observation_left_arm_6). The source index is renamed to sample_index.

Conversion details

  • Time = round(timestamp * 1000) with millisecond precision. timestamp is dropped because it is redundant (Time / 1000 seconds); frame_index is kept.
  • Rows are sorted by episode_index, task_index, then Time; Time is monotonic within each episode and restarts from zero at the first frame.
  • episode_index and task_index are stored as TsFile TAG/device dimensions, not duplicated as ordinary fields.
  • Numeric codec profile: FLOAT/DOUBLE → GORILLA, INT32/INT64 and Time → TS_2DIFF, all with LZ4 compression. No BOOLEAN source fields exist in this dataset; the configured BOOLEAN policy is RLE + LZ4.
  • Dropped/omitted source data: only redundant timestamp is dropped from the tabular rows; four video columns are omitted from TsFile and remain at the source URL above. No numeric rows or measurements are intentionally removed.

Reading 

from tsfile import TsFileReader

reader = TsFileReader("data/unitreerobotics_G1_Dex1_Clean_Table.tsfile")
table = next(iter(reader.get_all_table_schemas()))
columns = [c.get_column_name() for c in reader.get_all_table_schemas()[table].get_columns()
           if c.get_column_name() != "Time"]
with reader.query_table(table, columns, batch_size=65536) as result:
    batch = result.read_arrow_batch()

Local conversion and validation files

The dataset-specific script is D:\\code\\scripts\\convert_unitreerobotics_G1_Dex1_Clean_Table.py and the config is D:\\code\\config\\unitreerobotics_G1_Dex1_Clean_Table.yaml. Local validation reports are kept under conversion_reports/; they are not part of the upload set.