README.md

metadata

license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - robotics
  - tsfile
  - timeseries
  - format:tsfile
pretty_name: SAM Frames5 (TsFile)
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/sam_frames5_train.tsfile
modality:
  - tabular
  - timeseries

SAM Frames5 (TsFile)

This dataset is an Apache TsFile conversion of the Hugging Face dataset 1g0rrr/sam_frames5. The source was created with LeRobot and contains sam_double robot demonstrations for peeling the protective layer from adhesive tape.

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

Source Dataset

• Original dataset: 1g0rrr/sam_frames5
• License: apache-2.0
• LeRobot codebase version: v2.1
• Robot type: sam_double
• Task: Peel off the protective layer from the adhesive tape.
• Split: train (0:51)
• Scale: 51 episodes, 32,369 frames, 1 task
• Sampling rate: 30 fps
• Source frame files: 51 Parquet files
• Source videos: 153 MP4 files across 3 camera streams
• 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

The camera streams are observation.images.laptop, observation.images.phone, and observation.images.side. Each stream contains 51 videos at 30 fps. Source video frames are 480 x 640 RGB, encoded with AV1, and contain no audio.

Converted File

• TsFile: data/sam_frames5_train.tsfile
• TsFile table: sam_frames5_train
• Converted rows: 32,369
• Episodes: 51
• Time precision: milliseconds
• TAG columns: episode_index, task_index
• File size: 537,440 bytes
• SHA-256: 7415819c4cfe7786f3fe18ff489fe58e8c22abb1c7128c51ebc324c47c4bc788

All train episodes are merged into one TsFile. The source episode_index and task_index columns are retained as TAG columns, so queries can select an episode without synthetic aliases.

Schema

Time is computed as Time = round(timestamp * 1000) in milliseconds and restarts in each episode. At 30 fps, consecutive frames are approximately 33.333 ms apart. The source timestamp column is not retained 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)
• action_0 through action_6 (FLOAT)
• observation_state_0 through observation_state_6 (FLOAT)

The seven vector elements use this source order for both action and observation.state:

1. main_shoulder_pan
2. main_shoulder_lift
3. main_elbow_flex
4. main_wrist_flex
5. main_wrist_side
6. main_wrist_roll
7. main_gripper

Vector column names preserve the complete source feature name: . is replaced with _, then the zero-based element index is appended.

Video Policy

The three source video features are not converted or uploaded. Use the original dataset for synchronized videos: 1g0rrr/sam_frames5/videos.

Each numeric row retains episode_index, frame_index, task_index, and sample_index, preserving alignment with the original per-episode videos.

Metadata

The source meta/ files are mirrored in this repository. meta/info.json is updated so data_path points to data/sam_frames5_train.tsfile. Its tsfile_conversion object records the source and converted file counts, table name, Time formula, TAG columns, row count, feature mappings, and frame/video alignment. The converted total_videos is 0; the source count of 153 is preserved as tsfile_conversion.source_video_count.

Validation

The converted file was compared with all 51 source Parquet files and read back with the TsFile Python SDK:

• source and staged rows: 32,369
• duplicate (episode_index, task_index, Time) rows: 0
• maximum action-vector difference: 0
• maximum observation-state-vector difference: 0
• source index to converted sample_index mismatches: 0

Usage

from tsfile import TsFileReader

path = "data/sam_frames5_train.tsfile"
with TsFileReader(path) as reader:
    schemas = reader.get_all_table_schemas()
    table = schemas["sam_frames5_train"]
    print([(column.get_column_name(), column.get_category())
           for column in table.get_columns()])

Source & License

The source dataset is maintained at 1g0rrr/sam_frames5 and is distributed under the Apache License 2.0. The source card does not provide a paper, author list, or citation entry.