Datasets:

wangdx25
/

scripted_atomic_step_train_frac0_3_large_image

license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - lego-atomic-step
  - scripted
  - simulation
  - ur5
  - lerobot
  - v3
  - tsfile
  - timeseries
  - format:tsfile
pretty_name: scripted_atomic_step_train_frac0_3_large_image
modality: timeseries
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/scripted_atomic_step_train_frac0_3_large_image.tsfile

scripted_atomic_step_train_frac0_3_large_image (TsFile)

Apache TsFile version of windfromthenorth/scripted_atomic_step_train_frac0.3_large_image.

Overview

A LeRobot robot-manipulation dataset. The source card is auto-generated ("This dataset was created using LeRobot") and does not add a free-text description; the facts below are taken from the source meta/info.json.

Robot: ur5_wsg50_lego_atomic_step
Episodes: 664
Frames: 116214
Sampling rate: 20 fps
Splits: a single train split

Schema (TsFile structure)

Time (INT64, milliseconds) — round(timestamp * 1000); the source timestamp column (seconds) is dropped because it equals Time / 1000.
episode_index (TAG), task_index (TAG) — device dimensions; query one episode with WHERE episode_index = <n>.
frame_index (FIELD, INT64), sample_index (FIELD, INT64, from the source index) — per-frame bookkeeping.
Vector columns (single-precision FLOAT, source name with . → _ and an element index appended):
- observation.state → observation_state_0..observation_state_20 (FLOAT)
- action → action_0..action_6 (FLOAT)

Camera video streams are not included in this repository; see the original dataset for the videos: https://huggingface.co/datasets/windfromthenorth/scripted_atomic_step_train_frac0.3_large_image (videos/ directory).

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/scripted_atomic_step_train_frac0_3_large_image.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/windfromthenorth/scripted_atomic_step_train_frac0.3_large_image
Author / publisher: windfromthenorth
License: apache-2.0