README.md

metadata

language:
  - en
pretty_name: Vacuame/train4 (LeRobot SO-100) - TsFile
tags:
  - time-series
  - tsfile
  - robotics
  - lerobot
  - so100
  - manipulation
  - timeseries
modality: timeseries
task_categories:
  - robotics
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/vacuame_train4.tsfile

Vacuame/train4 (LeRobot SO-100) - TsFile

This dataset converts the numeric frame time-series from Vacuame/train4 to Apache TsFile format.

Modalities: Time-series. The original camera videos are not included in this converted repository; they remain in the source Hugging Face dataset.

Source Dataset

• Original dataset: Vacuame/train4
• LeRobot version: v2.0
• Robot type: so100
• Scale: 2 episodes, 119 frames, 1 task (try)
• Sampling rate: 30 fps
• Source frame data: data/chunk-000/episode_000000.parquet and data/chunk-000/episode_000001.parquet
• Source videos: 2 camera streams, observation.images.laptop and observation.images.phone, available in the source dataset videos/ tree
• License: the source dataset does not declare an explicit license

Converted Layout

README.md
data/vacuame_train4.tsfile
meta/info.json
meta/tasks.jsonl
meta/episodes.jsonl
meta/stats.json

The converted TsFile contains all 119 numeric frame rows in one table named vacuame_train4.

Schema

Role	Column(s)	Type	Notes
Time	Time	INT64 ms	round(timestamp * 1000)
TAG	episode_index, task_index	INT64	Source columns used as TsFile device tags
FIELD	frame_index, sample_index	INT64	index is renamed to sample_index
FIELD	action_0 ... action_5	FLOAT	Flattened from action[6]
FIELD	observation_state_0 ... observation_state_5	FLOAT	Flattened from observation.state[6]

Conversion Notes

• The generic lerobot converter was used.
• timestamp is dropped because it is redundant with Time / 1000 seconds.
• frame_index is kept so rows can be aligned back to source video frames.
• Vector columns preserve the source feature name with . replaced by _: observation.state becomes observation_state_0..observation_state_5, and action becomes action_0..action_5.
• Video features are omitted from the converted repository. Use the original dataset's videos/ tree for camera data.
• Source meta/ files are mirrored; meta/info.json is updated with a tsfile_conversion object documenting the converted path, schema roles, flattened features, dropped features, omitted videos, and row count.

Usage

from tsfile import TsFileReader

reader = TsFileReader("data/vacuame_train4.tsfile")
schemas = reader.get_all_table_schemas()
table_name = next(iter(schemas))

columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "observation_state_0",
    "action_0",
]

with reader.query_table(table_name, columns, batch_size=65536) as result:
    while (batch := result.read_arrow_batch()) is not None:
        df = batch.to_pandas()
        # Process rows here.

reader.close()

Citation

@misc{vacuame_train4,
  title     = {train4 (LeRobot SO-100)},
  author    = {Vacuame},
  url       = {https://huggingface.co/datasets/Vacuame/train4},
  publisher = {Hugging Face}
}