omx_f (TsFile)

Apache TsFile version of angela9355/hf_<redacted>.

Overview

A LeRobot robot dataset recorded on a omx_f arm. Task(s): pick up the thing. Each frame holds the commanded action and observed observation.state joint positions, plus camera views stored as videos in the original dataset.

• Episodes: 48
• Frames: 22,506
• Sampling rate: 30 fps
• Tasks: 1 — "pick up the thing"

Schema (TsFile structure)

All episodes share one TsFile with episode_index and task_index as TAG columns; query a single episode with WHERE episode_index = N.

• Time (INT64, milliseconds) — round(timestamp * 1000); the source timestamp column is dropped (it equals Time / 1000).
• episode_index (TAG) — device dimension.
• task_index (TAG) — device dimension.
• episode_index (INT64) — measurement.
• task_index (INT64) — measurement.
• frame_index (INT64) — measurement.
• sample_index (INT64) — measurement.
• observation_state_0 (FLOAT) — measurement.
• observation_state_1 (FLOAT) — measurement.
• observation_state_2 (FLOAT) — measurement.
• observation_state_3 (FLOAT) — measurement.
• observation_state_4 (FLOAT) — measurement.
• observation_state_5 (FLOAT) — measurement.
• action_0 (FLOAT) — measurement.
• action_1 (FLOAT) — measurement.
• action_2 (FLOAT) — measurement.
• action_3 (FLOAT) — measurement.
• action_4 (FLOAT) — measurement.
• action_5 (FLOAT) — measurement.

The vector columns are flattened per joint:

• action_* — commanded joints: joint1, joint2, joint3, joint4, joint5, gripper_joint_1.
• observation_state_* — observed joints: joint1, joint2, joint3, joint4, joint5, gripper_joint_1.

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/omx_f.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/angela9355/hf_
• Author / publisher: angela9355
• License: apache-2.0
• Note: camera videos are NOT included; see the original dataset.