Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    ValueError
Message:      Some splits are duplicated in data_files: ['train', 'train', 'train', 'train', 'train', 'train']
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  config_names = get_dataset_config_names(
                      path=dataset,
                      token=hf_token,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                      path,
                  ...<4 lines>...
                      **download_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
                  ).get_module()
                    ~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 646, in get_module
                  patterns = sanitize_patterns(next(iter(metadata_configs.values()))["data_files"])
                File "/usr/local/lib/python3.14/site-packages/datasets/data_files.py", line 151, in sanitize_patterns
                  raise ValueError(f"Some splits are duplicated in data_files: {splits}")
              ValueError: Some splits are duplicated in data_files: ['train', 'train', 'train', 'train', 'train', 'train']

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

mug_hang_mp_500 (TsFile)

This repository contains a TsFile conversion of the Hugging Face dataset younghyopark/mug_hang_mp_500, a LeRobot-format robotics dataset created with LeRobot.

Modalities: Time-series.

Source Dataset

  • Original dataset: younghyopark/mug_hang_mp_500
  • License: apache-2.0
  • Robot type: DualPanda
  • Codebase version: v2.1
  • Episodes: 500
  • Frames: 591,679
  • Tasks: 1
  • FPS: 50
  • Original data path pattern: data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
  • Original video path: None; total videos: 0

Converted Files

  • Converted rows: 591,679

  • TsFile shards: 6

  • Total TsFile size: 939,574,292 bytes

  • Table name: mug_hang_mp_500

  • Converted data path pattern recorded in meta/info.json: data/mug_hang_mp_500_*.tsfile

  • data/mug_hang_mp_500_1.tsfile (158,338,984 bytes)

  • data/mug_hang_mp_500_2.tsfile (158,768,860 bytes)

  • data/mug_hang_mp_500_3.tsfile (159,295,248 bytes)

  • data/mug_hang_mp_500_4.tsfile (159,249,339 bytes)

  • data/mug_hang_mp_500_5.tsfile (158,149,388 bytes)

  • data/mug_hang_mp_500_6.tsfile (145,772,473 bytes)

TsFile Schema Design

  • Time is the TsFile time column in millisecond precision.
  • episode_index and task_index are TAG columns. They identify the LeRobot episode and task.
  • FIELD columns: 652 scalar measurements after flattening vector and matrix features.
  • The original timestamp field is dropped because Time = round(timestamp * 1000) and therefore represents the same value in milliseconds.
  • The original index column is renamed to sample_index; frame_index is retained.
  • Source feature names containing . are converted to _ in TsFile field names.

Flattening examples

Source feature Converted field range Count
observation.joint observation_joint_0 ... observation_joint_15 16
observation.state observation_state_0 ... observation_state_29 30
observation.environment_state observation_environment_state_0 ... observation_environment_state_13 14
observation.ee_pose observation_ee_pose_0 ... observation_ee_pose_15 16
action.ctrl action_ctrl_0 ... action_ctrl_15 16
action.joint action_joint_0 ... action_joint_15 16
action.ee_pose action_ee_pose_0 ... action_ee_pose_15 16

Array2D-style matrix features are flattened in row-major order. For example, a 3x3 matrix becomes 9 scalar FLOAT fields with suffixes _0 through _8.

Metadata

The source meta/ directory is mirrored under meta/. The file meta/info.json has been rewritten so that:

  • data_path points to data/mug_hang_mp_500_*.tsfile instead of the original Parquet pattern.
  • features describes the converted TsFile scalar schema and roles (TIME, TAG, FIELD).
  • tsfile_conversion records the source dataset, time mapping, row count, flattened features, renamed fields, and dropped fields.

Reading

from tsfile import TsFileReader

reader = TsFileReader("data/mug_hang_mp_500_1.tsfile")
# Inspect available table schemas or query table data with the Apache TsFile Python SDK.
print(reader.get_all_table_schemas())
reader.close()

Conversion Notes

Conversion notes:

  • LeRobot-style robot dataset converted to TsFile via the generic lerobot converter.
  • Source metadata declares robot_type=DualPanda, 500 episodes, 591,679 frames, 1 task, 50 fps, and Apache-2.0 license.
  • Numeric frame data is stored as one logical TsFile table under data/. Because this dataset is wide, local conversion uses bounded Parquet row groups and may emit data/mug_hang_mp_500_*.tsfile shards.
  • Source columns episode_index and task_index are TAG columns; no synthetic episode_id or task_id columns are created. Query a single episode with WHERE episode_index=N.
  • Vector columns preserve the full source column name when flattened: . is replaced with _ and the element index is appended. Values are stored as single-precision FLOAT fields.
  • Time is synthesized as round(timestamp * 1000) milliseconds. The source timestamp column is not retained as a separate field because it equals Time / 1000 seconds; frame_index is kept.
  • Source metadata has total_videos=0 and video_path=null; no videos are included in this converted repository.
  • meta/ is mirrored from the source except that meta/info.json describes the converted TsFile schema and records the original video path separately.
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