| --- |
| license: mit |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| - tsfile |
| - format:tsfile |
| - timeseries |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
|
|
|
|
| # lerobot_pusht (TsFile format) |
| |
| > This dataset is a conversion of the Hugging Face dataset [**lerobot/pusht**](https://huggingface.co/datasets/lerobot/pusht) to [Apache TsFile](https://tsfile.apache.org/) format. |
| > **Original dataset**: https://huggingface.co/datasets/lerobot/pusht |
| |
| ## Dataset Description |
| |
| Push-T is a robot-manipulation benchmark task introduced by **Diffusion Policy** (Chi et al. 2023): the agent must push a T-shaped block to a target pose. This dataset was collected with [LeRobot](https://github.com/huggingface/lerobot) (codebase v2.0) and is its **low-dimensional state version** — time series of 2-D end-effector state and 2-D action. |
| |
| - **Homepage**: https://diffusion-policy.cs.columbia.edu/ |
| - **Paper**: https://arxiv.org/abs/2303.04137v5 |
| - **License**: MIT |
| - **Size**: 206 episodes / 25,650 frames / 1 task |
| - **Sampling rate**: 10 fps (100 ms per frame) |
| |
| ## Original Data Structure |
| |
| | Column | Type | Description | |
| |----|------|------| |
| | `observation.state` | float32[2] | End-effector state (x, y) | |
| | `action` | float32[2] | Action (x, y) | |
| | `episode_index` | int64 | Episode index | |
| | `frame_index` | int64 | Frame index within the episode | |
| | `timestamp` | float32 | Seconds elapsed within the episode | |
| | `next.reward` | float32 | Reward | |
| | `next.done` / `next.success` | bool | Termination / success flags | |
| | `index` | int64 | Global sample index | |
| | `task_index` | int64 | Task index | |
| | `observation.image` | video[96×96×3] | (**not included** — this low-dimensional version has no images) | |
|
|
| ## TsFile Conversion Notes |
|
|
| Conversion uses the "script preprocessing + Apache TsFile Java tool (schema mode)" path: |
|
|
| - **Array expansion**: `observation.state[2]` → `state_0`, `state_1`; `action[2]` → `action_0`, `action_1` (kept as float32 → TsFile `FLOAT`). |
| - **Column-name cleanup**: `.` is replaced with `_` (`next.reward` → `next_reward`, etc.). |
| - **Time axis**: `Time = frame_index × 100 ms` (10 fps), millisecond precision. |
| - **Tag columns (device dimension)**: `episode_id`, `task_id` are declared as TsFile **TAG**, so each episode is an independent device with its own time axis starting at 0. |
| - **⚠️ Dropped columns**: the original `timestamp` (per-episode elapsed seconds, float) is dropped because it repeats across episodes; an integer-millisecond time axis (`frame_index × 100 ms`) is used instead. All other columns are retained. |
|
|
| The converted table is named `pusht`, in a single file `lerobot_pusht.tsfile` with 25,650 rows. |
|
|
| ## Usage |
|
|
| ```python |
| # Read lerobot_pusht.tsfile with the Apache TsFile SDK |
| from tsfile import TsFileReader |
| reader = TsFileReader("lerobot_pusht.tsfile") |
| # table "pusht": tag columns episode_id / task_id, remaining columns are field measurements |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{chi2024diffusionpolicy, |
| author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song}, |
| title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion}, |
| journal = {The International Journal of Robotics Research}, |
| year = {2024}, |
| } |
| ``` |
|
|
