Datasets:

zjt24
/

earbud_case_sequential_insertion_teleop

license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - tsfile
  - timeseries
  - robotics
  - manipulation
  - teleoperation
  - tactile
  - format:tsfile
modality:
  - tabular
  - timeseries
pretty_name: earbud_case_sequential_insertion_teleop TsFile
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/earbud_case_sequential_insertion_teleop.tsfile

earbud_case_sequential_insertion_teleop TsFile

This dataset is a TsFile conversion of the Hugging Face dataset Xense/earbud_case_sequential_insertion_teleop, which was created using LeRobot.

Modalities: Time-series. The original dataset also includes camera and tactile video streams; those videos are not included in this repository and remain available in the source dataset.

Source Dataset

Original dataset: Xense/earbud_case_sequential_insertion_teleop
License: Apache-2.0
Task category: robotics
Robot type: bi_flexiv_rizon4_rt
Task: pick up each earbud case from the left stands, insert the matching earbuds, close the lid, and place the case on the middle stand.
Split: single train split (0:85)
Scale: 85 episodes, 448,367 frames, 1 task, 31 source frame Parquet files
Sampling rate: 30 fps
Original video features:
- observation.images.head
- observation.images.left_wrist
- observation.images.right_wrist
- observation.images.left_tactile_0
- observation.images.left_tactile_1
- observation.images.right_tactile_0
- observation.images.right_tactile_1

Converted Files

TsFile: data/earbud_case_sequential_insertion_teleop.tsfile
Rows: 448,367
Episodes: 85
Tasks: 1 (task_index = 0)
Table name: earbud_case_sequential_insertion_teleop
Time precision: milliseconds
Metadata: meta/ is mirrored from the source dataset, with meta/info.json rewritten to describe the TsFile artifact and conversion mapping.

Schema

Column	Role	Type	Notes
`Time`	TIME	INT64	`round(timestamp * 1000)`, in milliseconds; restarts per episode
`episode_index`	TAG	INT64	Source episode identifier
`task_index`	TAG	INT64	Source task identifier
`frame_index`	FIELD	INT64	Source frame index, preserved
`sample_index`	FIELD	INT64	Renamed from source `index`
`action_0` ... `action_19`	FIELD	FLOAT	Flattened from `action[20]`
`observation_state_0` ... `observation_state_19`	FIELD	FLOAT	Flattened from `observation.state[20]`

The action and observation-state element names are: left_tcp.x, left_tcp.y, left_tcp.z, left_tcp.r1 ... left_tcp.r6, right_tcp.x, right_tcp.y, right_tcp.z, right_tcp.r1 ... right_tcp.r6, left_gripper.pos, and right_gripper.pos.

episode_index and task_index are TAG columns, so they are available as TsFile table tag dimensions. To read one episode, filter by episode_index.

Conversion Notes

The LeRobot frame Parquet files under data/chunk-000/ were merged into one TsFile for the train split.
Vector columns were flattened by preserving the source column name, replacing . with _, and appending the element index.
The source timestamp column is dropped because it is redundant with Time / 1000 seconds.
The source index column is renamed to sample_index.
Camera and tactile videos are not uploaded here. Use the original dataset videos: https://huggingface.co/datasets/Xense/earbud_case_sequential_insertion_teleop/tree/main/videos

Read Example

from tsfile import TsFileReader

path = "data/earbud_case_sequential_insertion_teleop.tsfile"
table = "earbud_case_sequential_insertion_teleop"

with TsFileReader(path) as reader:
    columns = [
        "episode_index",
        "task_index",
        "frame_index",
        "sample_index",
        "action_0",
        "observation_state_0",
    ]
    with reader.query_table(table, columns, batch_size=4096) as result:
        batch = result.read_arrow_batch()
        print(batch)

Citation

The original dataset card does not provide a BibTeX citation.