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license: mit
language:
- en
tags:
- robotics
- simulation
- wet-op-bench
---
# WET-OP Bench — Training Dataset
This dataset contains 7,000 simulated robot demonstrations for 14 scientific experiment tasks, with 500 episodes per task. The robot is Lift2. Data are stored in LeRobot v2.1 format, with four RGB camera streams, robot states, actions, timestamps, and language task descriptions.
Each top-level task folder is an independent LeRobot dataset root. Its `data/`, `meta/`, and `videos/` directories must remain together. Episode indices are local to each task; this release preserves the original indices and metadata. Images are 640 × 480 and the recorded video rate is 15 FPS.
| Task folder | Episodes | Frames |
| --- | ---: | ---: |
| task_01_pour_flask_to_cylinder | 500 | 1082240 |
| task_02_pour_liquid_and_stir | 500 | 1250790 |
| task_03_magnetic_beaker_stirrer | 500 | 924400 |
| task_04_pour_liquid_with_funnel | 500 | 1732500 |
| task_05_remove_container_lid | 500 | 364780 |
| task_06_remove_centrifuge_tubes | 500 | 1825520 |
| task_07_insert_centrifuge_tubes | 500 | 1290560 |
| task_08_place_container_in_oven | 500 | 2066180 |
| task_09_solid_weighing | 500 | 1062420 |
| task_10_titration_experiment | 500 | 484260 |
| task_11_remove_container_from_oven | 500 | 1729620 |
| task_12_fehling_test | 500 | 2211640 |
| task_13_blue_bottle_reaction | 500 | 2154840 |
| task_14_fixed_weight_weighing | 500 | 1474800 |
## Download and use
```python
from huggingface_hub import snapshot_download
path = snapshot_download(
repo_id="InternRobotics/WET-OP-Bench-Train",
repo_type="dataset",
local_dir="WET-OP-Bench-Train",
)
```
To download one task, add `allow_patterns=["task_01_pour_flask_to_cylinder/**", "README.md", "LICENSE", "manifest.jsonl"]`. Point a LeRobot v2.1-compatible reader at that task folder. This repository contains multiple task roots, rather than one concatenated dataset. Feature names, shapes, camera keys and per-episode lengths are described by each task's `meta/info.json` and `meta/episodes.jsonl`.
The release was checked for file integrity, episode/frame index consistency, referenced video presence, and sample video decoding. These checks do not constitute a fresh replay or task-success evaluation of every trajectory.
## Related dataset
[WET-OP-Bench-Validation](https://huggingface.co/datasets/InternRobotics/WET-OP-Bench-Validation) provides one task-01 simulator initialization example without recorded trajectories. It is not the full benchmark evaluation set.
## Provenance and license
The scene and model assets were created internally by the AI Lab team. The released training data are distributed under the MIT License; see `LICENSE`. This repository does not redistribute the simulator SDK.
## Release status
Verified: all published payload file sizes and content hashes were checked against the local release.
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