---
license: mit
task_categories:
- robotics
language:
- en
tags:
- LeRobot
- ALOHA
- Robotics
- Manipulation
- tsfile
- timeseries
- format:tsfile
modality:
- tabular
- timeseries
pretty_name: ALOHA Dining Table TsFile
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/aloha_dining_table.tsfile
---

# ALOHA Dining Table TsFile

This dataset is a TsFile conversion of the Hugging Face dataset
[`htrbao/aloha-dining-table`](https://huggingface.co/datasets/htrbao/aloha-dining-table),
which uses the LeRobot v2.1 format.

Modalities: Time-series. The original dataset also includes three video streams,
`observation.images.color.high`, `observation.images.color.wrist_right`, and
`observation.images.color.wrist_left`; those videos are not included in this
repository and remain available in the source dataset.

## Source Dataset

- Original dataset: [`htrbao/aloha-dining-table`](https://huggingface.co/datasets/htrbao/aloha-dining-table)
- License: MIT
- Format: LeRobot v2.1
- Robot type: `ALOHA`
- Task: `Spread the dining mat on the table, place the plate at the center of the mat, put the spoon to the left of the plate, and place the chopsticks to the right of the plate.`
- Split: single `train` split (`0:99`)
- Scale: 99 episodes, 80,041 frames, 1 task, 1 data chunk
- Sampling rate: 15 fps
- Original video features:
- `observation.images.color.high` at 480x640x3
- `observation.images.color.wrist_right` at 480x640x3
- `observation.images.color.wrist_left` at 480x640x3

## Converted Files

- TsFile: `data/aloha_dining_table.tsfile`
- Rows: 80,041
- Episodes: 99
- Tasks: 1 (`task_index = 0`)
- Table name: `aloha_dining_table`
- 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_13` | FIELD | FLOAT | Flattened from `action[14]` |
| `observation_state_0` ... `observation_state_13` | FIELD | FLOAT | Flattened from `observation.state[14]` |
| `observation_velocity_0` ... `observation_velocity_13` | FIELD | FLOAT | Flattened from `observation.velocity[14]` |
| `observation_effort_0` ... `observation_effort_13` | FIELD | FLOAT | Flattened from `observation.effort[14]` |

The 14 action and observation state elements correspond to the ALOHA joint
displacements named in the source metadata: `left_waist`, `left_shoulder`,
`left_elbow`, `left_forearm_roll`, `left_wrist_angle`, `left_wrist_rotate`,
`left_gripper`, `right_waist`, `right_shoulder`, `right_elbow`,
`right_forearm_roll`, `right_wrist_angle`, `right_wrist_rotate`, and
`right_gripper`.

`episode_index` and `task_index` are TAG columns, so they form the TsFile device
dimension. 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`.
- Videos are not uploaded here. Use the original dataset videos:
  https://huggingface.co/datasets/htrbao/aloha-dining-table/tree/main/videos

## Read Example

```python
from tsfile import TsFileReader

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

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)
```