---
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`](https://huggingface.co/datasets/Xense/earbud_case_sequential_insertion_teleop),
which was created using [LeRobot](https://github.com/huggingface/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`](https://huggingface.co/datasets/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

```python
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.