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60 episodes · 30 fps

cafe_001 (TsFile)

Apache TsFile version of kjydb/cafe_001.

Overview

A teleoperation dataset recorded on a PS-A-03 500 arm and packaged in the LeRobot v2.1 layout. Each row is one control step: a 13-dimensional Cartesian state (position x, y, z, orientation rx, ry, rz, linear velocities x.vel, y.vel, z.vel, angular velocities pitch.vel, roll.vel, yaw.vel, and gripper.status) and the matching 13-dimensional commanded action. The metadata records a single task (task_index = 0).

  • Robot: PS-A-03 500
  • Episodes: 60 · Frames (rows): 36,101 · Tasks: 1
  • Sampling rate: 30 fps
  • Cameras: observation.images.wrist (480x640x3, AV1)
  • Converted TsFile: 36,101 rows in a single data/cafe_001.tsfile, one device per episode_index (WHERE episode_index=0 selects episode 0).

Schema (TsFile structure)

  • Time (INT64, milliseconds) — round(timestamp * 1000); restarts at 0 for each episode.
  • episode_index (TAG) — episode / device dimension.
  • task_index (TAG) — task dimension (constant 0 here).
  • frame_index (FIELD, INT64) — source frame counter; sample_index (FIELD, INT64) — source index column (renamed).
  • action_{0..12} (FIELD, FLOAT) — commanded Cartesian pose/velocity + gripper target
  • observation.state_{0..12} (FIELD, FLOAT) — measured Cartesian pose/velocity + gripper status

Vector columns were flattened to scalar fields: a . in the source column name became _ and the element index is appended (e.g. observation.state → observation_state_0 ... observation_state_{n-1}). Values are single-precision FLOAT. The source timestamp column is dropped because it equals Time ÷ 1000 seconds. No other columns or rows were removed.

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/cafe_001.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

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