---
license: apache-2.0
authors:
- 356c
task_categories:
- robotics
tags:
- tsfile
- timeseries
- tabular
modality:
- timeseries
- tabular
pretty_name: SO100 Nut Test 11 TsFile
configs:
- config_name: default
  data_files:
  - split: train
    path: data/356c_so100_nut_test_11.tsfile
size_categories:
- 10K<n<100K
---

# SO100 Nut Test 11 TsFile

This dataset is an Apache TsFile conversion of
[`356c/so100_nut_test_11`](https://huggingface.co/datasets/356c/so100_nut_test_11), a LeRobot v2.1 SO100
robot-manipulation dataset for picking up a nut and dropping it in a cup.

## Source Dataset and Attribution

- Original dataset: [`356c/so100_nut_test_11`](https://huggingface.co/datasets/356c/so100_nut_test_11)
- Original author, repository owner, uploader, and sole contributor:
  [356c](https://huggingface.co/356c)
- License: Apache-2.0
- Task: "Pick up nut and drop in cup."
- Robot: `so100`; LeRobot version: `v2.1`
- Split: `train`; sampling rate: 30 fps
- Scale: 100 episodes, 38,691 frames, 1 task
- Source shards: 100 Parquet files totaling 2,190,148 bytes
- Source layout: `data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet`
- The source card provides no paper, external homepage, or completed citation.

## TsFile Data

- Path: `data/356c_so100_nut_test_11.tsfile`
- Table: `356c_so100_nut_test_11`
- Rows: 38,691; episodes/devices: 100; tasks: 1
- Time precision: milliseconds
- TsFile/source-Parquet size ratio: 0.330

## Schema and Mapping

`Time = round(timestamp * 1000)` milliseconds. Time starts at zero and is
strictly increasing in every episode. The source `timestamp` is omitted after
conversion because it is recoverable as `Time / 1000` seconds.

| TsFile column | Role | Type | Source mapping |
|---|---|---|---|
| `Time` | TIME | TIMESTAMP | `round(timestamp * 1000)` ms |
| `episode_index` | TAG | STRING | Original INT64 episode index |
| `task_index` | TAG | STRING | Original INT64 task index |
| `frame_index` | FIELD | INT64 | Preserved |
| `sample_index` | FIELD | INT64 | Renamed from `index` |
| `action_0` ... `action_5` | FIELD | FLOAT | Flattened from `action[6]` |
| `observation_state_0` ... `observation_state_5` | FIELD | FLOAT | Flattened from `observation.state[6]` |

The six vector dimensions are `main_shoulder_pan`, `main_shoulder_lift`,
`main_elbow_flex`, `main_wrist_flex`, `main_wrist_roll`, and `main_gripper`.
Dots in source names are replaced by underscores. No numeric row, episode,
task, action dimension, or state dimension is dropped.

## Encodings and Compression

- FLOAT/DOUBLE: GORILLA + LZ4
- INT32/INT64: TS_2DIFF + LZ4
- Time: TS_2DIFF + LZ4
- BOOLEAN: RLE + LZ4 (the source has no BOOLEAN field)
- TAG: TsFile table/device TAG storage

## Videos

Videos are not included in this TsFile dataset. The 300 original AV1 MP4 files
remain in the source repository, 640x480 at 30 fps with no audio, in three
streams with 100 episode files each:

- [`observation.images.body`](https://huggingface.co/datasets/356c/so100_nut_test_11/tree/main/videos/chunk-000/observation.images.body)
- [`observation.images.overhead`](https://huggingface.co/datasets/356c/so100_nut_test_11/tree/main/videos/chunk-000/observation.images.overhead)
- [`observation.images.side`](https://huggingface.co/datasets/356c/so100_nut_test_11/tree/main/videos/chunk-000/observation.images.side)

The source layout is
`videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4`.
Use `episode_index` and `frame_index` to align numeric rows with video frames.

## Minimal Read Example

```python
from tsfile import TsFileReader

reader = TsFileReader("data/356c_so100_nut_test_11.tsfile")
with reader.query_table(
    "356c_so100_nut_test_11",
    ["episode_index", "task_index", "frame_index", "sample_index",
     "action_0", "observation_state_0"],
    batch_size=65536,
) as result:
    batch = result.read_arrow_batch()
    print(batch.to_pandas().head())
reader.close()
```

## Citation

The source card supplies no paper or completed citation. Cite the original
Hugging Face dataset and its publisher, `356c`, when using this conversion.