| ---
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| license: other
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| language:
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| - en
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| pretty_name: tutorial-ball-2 (LeRobot) — TsFile
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| tags:
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| - time-series
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| - tsfile
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| - robotics
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| - lerobot
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| - imitation-learning
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| - timeseries
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| task_categories:
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| - time-series-forecasting
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| - robotics
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| ---
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|
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| # tutorial-ball-2 (LeRobot) — TsFile
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|
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| This dataset is a **lossless conversion to the [Apache TsFile](https://tsfile.apache.org/)
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| format** of the HuggingFace LeRobot dataset
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| [`notmahi/tutorial-ball-2`](https://huggingface.co/datasets/notmahi/tutorial-ball-2):
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| a low-dimensional robot tutorial trajectory dataset (**no video**).
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|
|
| ## Original dataset
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|
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| - **Source dataset**: [notmahi/tutorial-ball-2](https://huggingface.co/datasets/notmahi/tutorial-ball-2)
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| - **Format**: early LeRobot format (`meta_data/` + safetensors)
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| - **Content**: purely numeric low-dimensional state/action trajectories —
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| **314,074 frames / 751 episodes / 30 fps**. No images or video
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| (`meta_data/info.json`: `video=0`).
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|
|
| ## What is in this repository
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|
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| ```
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| data/
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| └── tutorial_ball_2.tsfile # numeric time-series (converted)
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| meta_data/
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| ├── info.json # original fps/video flags + tsfile_conversion notes
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| ├── stats.safetensors # per-feature statistics (copied verbatim)
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| └── episode_data_index.safetensors # episode boundaries (copied verbatim)
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| ```
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|
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| ## TsFile storage mapping (table model)
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|
|
| | Role | Column(s) | Type | Notes |
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| |------|-----------|------|-------|
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| | **TAG** | `episode_id` | STRING | `episode_{episode_index}`, 751 devices (one per episode) |
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| | **Time** | `round(frame_index * 1000 / 30)` ms | INT64 (ms) | 30 fps; frame_index restarts at 0 each episode |
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| | **FIELD** | `state_0` … `state_3` | FLOAT | `observation.state[4]` expanded |
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| | **FIELD** | `action_0`, `action_1` | FLOAT | `action[2]` expanded |
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| | **FIELD** | `episode_index`, `frame_index`, `sample_index` | INT64 | indices (`index` → `sample_index`) |
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| | **FIELD** | `episode_timestamp_s` | FLOAT | (`timestamp`) |
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| | **FIELD** | `next_done` | BOOLEAN | (`next.done`) |
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|
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| ## Conversion notes
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|
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| - **Purely numeric** — the source has no images or video, so only `data/` is
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| converted; nothing else needed.
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| - **TAG = `episode_id`** (751 devices). **Time = `round(frame_index × 1000/30)` ms**.
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| Because `frame_index` restarts at 0 within each episode and is strictly increasing,
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| and `round(k × 1000/30)` is also strictly increasing in `k` (step ≥ 33 ms), every
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| device's time axis is strictly increasing — no de-duplication or offset needed.
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| (30 fps gives a ~33.333 ms frame interval; with millisecond precision the per-frame
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| times are 0, 33, 67, 100, … — consecutive and collision-free.)
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| - **Array columns expanded**: `observation.state[4]` → `state_0..state_3`,
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| `action[2]` → `action_0..action_1` (FLOAT, matching the source float32).
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| - **Column names** with dots made TsFile-safe (`next.done` → `next_done`, …).
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| - **No columns dropped, no rows dropped**: all 314,074 frames preserved.
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| - `meta_data/` (info / stats / episode index) is copied over; `info.json` gains a
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| `tsfile_conversion` block describing the table layout.
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|
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| ## Usage
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|
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| ```python
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| from tsfile import TsFileReader
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|
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| reader = TsFileReader("data/tutorial_ball_2.tsfile")
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| schemas = reader.get_all_table_schemas()
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| tname = next(iter(schemas))
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|
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| cols = ["episode_id", "state_0", "state_1", "action_0", "action_1"]
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| with reader.query_table(tname, cols, batch_size=65536) as rs:
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| while (batch := rs.read_arrow_batch()) is not None:
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| df = batch.to_pandas()
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| # ... process ...
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| reader.close()
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| ```
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|
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| ## Citation
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|
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| ```bibtex
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| @misc{tutorial_ball_2,
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| title = {tutorial-ball-2 (LeRobot)},
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| author = {notmahi},
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| url = {https://huggingface.co/datasets/notmahi/tutorial-ball-2},
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| publisher = {Hugging Face}
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| }
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| ```
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|
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| The source HuggingFace dataset does not declare an explicit license.
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|
|
