README.md

metadata

license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - robotics
  - tsfile
  - timeseries
  - format:tsfile
pretty_name: Random50 v3 (TsFile)
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/random50_v3_train.tsfile
modality:
  - tabular
  - timeseries

Random50 v3 (TsFile)

This dataset is an Apache TsFile conversion of the Hugging Face dataset iiyudana/random50_v3. The source dataset was created with LeRobot and contains ALOHA robot demonstrations for transferring a cube between arms.

Modalities: Time-series. The original repository also contains synchronized top-camera videos; videos are not included in this converted repository.

Source Dataset

• Original dataset: iiyudana/random50_v3
• License: apache-2.0
• LeRobot codebase version: v2.1
• Robot type: aloha
• Task: Pick up the cube with the right arm and transfer it to the left arm.
• Split: train (0:50)
• Scale: 50 episodes, 20,000 frames, 1 task
• Sampling rate: 50 fps
• Source videos: 50 MP4 files from observation.images.top
• Source data layout: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
• Source video layout: videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4

Converted File

• TsFile: data/random50_v3_train.tsfile
• TsFile table: random50_v3_train
• Converted rows: 20,000
• Episodes: 50
• Time precision: milliseconds
• TAG columns: episode_index, task_index
• File size: 2,195,882 bytes

All source episodes in the train split are merged into one TsFile. The source episode_index and task_index columns are retained as TAG columns, allowing queries to select an episode without creating synthetic tag aliases.

Schema

Time is computed as round(timestamp * 1000) in milliseconds and restarts in each episode. At 50 fps, consecutive source frames are approximately 20 ms apart. The source timestamp column is dropped because it is redundant with Time / 1000 seconds. No source rows are dropped.

TAG columns:

• episode_index
• task_index

FIELD columns:

• frame_index
• sample_index (renamed from source index)
• observation_state_0 through observation_state_13 (FLOAT)
• action_0 through action_13 (FLOAT)

The 14 elements in both observation.state and action use this source order:

1. left_waist
2. left_shoulder
3. left_elbow
4. left_forearm_roll
5. left_wrist_angle
6. left_wrist_rotate
7. left_gripper
8. right_waist
9. right_shoulder
10. right_elbow
11. right_forearm_roll
12. right_wrist_angle
13. right_wrist_rotate
14. right_gripper

Vector names preserve the full source feature name: . is replaced with _ and the element index is appended.

Video Policy

The source feature observation.images.top is not converted or uploaded. It is 480 x 640 RGB AV1 video at 50 fps. Use the original dataset for the synchronized videos: iiyudana/random50_v3/videos.

The numeric rows retain episode_index, frame_index, task_index, and sample_index, preserving their alignment with the original per-episode video.

Metadata

The source meta/ files are mirrored in this repository. meta/info.json is updated so data_path points to data/random50_v3_train.tsfile. Its tsfile_conversion object records the actual table name, source episode-file count, converted TsFile count, Time formula, TAG columns, row count, flattened features, renamed and dropped fields, and frame/video alignment. The converted total_videos value is 0; the original count of 50 is preserved as tsfile_conversion.source_video_count.

Validation

The converted file was validated with the project pipeline and read back with the TsFile Python SDK:

• staged Parquet rows: 20,000
• TsFile metadata rows: 20,000
• TsFile query rows: 20,000
• duplicate (episode_index, task_index, Time) rows: 0
• TsFile size: 2,195,882 bytes

Usage

from tsfile import TsFileReader

path = "data/random50_v3_train.tsfile"
with TsFileReader(path) as reader:
    schemas = reader.get_all_table_schemas()
    table = schemas["random50_v3_train"]
    print([(column.get_column_name(), column.get_category())
           for column in table.get_columns()])