THULab/so101_pencil_holder_1

SO-101 Pencil Holder 1 (TsFile)

Apache TsFile version of nikodembartnik/so101_pencil_holder_1.

Overview

A SO follower dataset for the task "put the pen in the wooden box" (source pretty name: "Task: put pen in wooden box"). Each frame records the 6-D joint state and the 6-D commanded action.

• Scale: 200 episodes, 75,592 frames, 1 task, sampled at 30 fps; one train split in data/so101_pencil_holder_1.tsfile.

Task (task_index 0): "put the pen in the wooden box".

Schema (TsFile structure)

• Time (INT64, milliseconds) - per-episode sample time, round(timestamp * 1000) in milliseconds (the source timestamp column equals Time / 1000 seconds and is therefore not stored separately).
• episode_index, task_index (TAG) - device dimensions identifying one demonstration episode and one task; query a single episode with WHERE episode_index=0.
• action_0..5, observation_state_0..5 (FIELD) - FLOAT 6-D SO-follower joint action and 6-D observation state.
• frame_index, sample_index (INT64, FIELD) - source frame counter and source row counter (index renamed to sample_index).

The dataset is stored as one wide TsFile table; within the train split all episodes share that single file with episode_index and task_index as TAG columns. State/action vectors are flattened to one FLOAT field per element (. -> _, element index appended). Camera video files are not included in this repository; the original dataset's videos are at videos/. meta/ is mirrored from the source. No data rows or non-video columns are dropped, apart from the redundant source timestamp column (replaced by Time). Two camera image/video features (observation.images.top, observation.images.wrist) are omitted as non-time-series.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("data/so101_pencil_holder_1.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license

• Original dataset: https://huggingface.co/datasets/nikodembartnik/so101_pencil_holder_1
• Author / publisher: nikodembartnik
• License: apache-2.0