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Datasets:
THULab
/
franka_multi_task_v1_vid

Tasks:
Robotics
Modalities:
Tabular
Time-series
Formats:
parquet
Size:
< 1K
Tags:
tsfile
timeseries
time-series
LeRobot
robotics
format:tsfile
Libraries:
Datasets
Dask
Polars
License:
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episode_index
int64
length
int64
dataset_from_index
int64
dataset_to_index
int64
data/chunk_index
int64
data/file_index
int64
videos/observation.images.wrist_camera/chunk_index
int64
videos/observation.images.wrist_camera/file_index
int64
videos/observation.images.wrist_camera/from_timestamp
float64
videos/observation.images.wrist_camera/to_timestamp
float64
videos/observation.images.side_camera/chunk_index
int64
videos/observation.images.side_camera/file_index
int64
videos/observation.images.side_camera/from_timestamp
float64
videos/observation.images.side_camera/to_timestamp
float64
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488
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459
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franka_multi_task_v1_vid (TsFile)

Apache TsFile version of Beegbrain/franka_multi_task_v1_vid.

Overview

A LeRobot v3.0 robot manipulation dataset covering 4 tasks. Each frame holds the commanded action and observed observation.state joint positions; camera views are stored as videos in the original dataset.

  • Episodes: 200
  • Frames: 75,843
  • Sampling rate: 10 fps
  • Tasks: 4
  • Split: a single train split (0:200)
  • Robot: not declared in the source metadata

Schema (TsFile structure)

All episodes share one TsFile with episode_index and task_index as TAG columns; query a single episode with WHERE episode_index = N.

  • Time (INT64, milliseconds) — round(timestamp * 1000); the source timestamp column is dropped (it equals Time / 1000).
  • episode_index (TAG) — device dimension.
  • task_index (TAG) — device dimension.
  • frame_index (INT64) — measurement.
  • sample_index (INT64) — measurement.
  • observation_state_0..7 (FLOAT) — measured joint positions and gripper.
  • action_0..7 (FLOAT) — commanded joint positions and gripper.

The vector columns are flattened per joint (joint_0..joint_6, gripper per the source meta/info.json feature names).

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/franka_multi_task_v1_vid.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

Downloads last month
53
Number of rows:
204
Total file size:
4.29 MB