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license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- manipulation
- pick-and-place
- cafe-serving
- omx
size_categories:
- 100K<n<1M
---
# serving_a — Café Pick-and-Place (Long Episode)
LeRobot **v3.0** dataset for OpenManipulator-X (OMX) cafe serving task.
This is the **Category A (Long Episode)** subset of the GR00T N1.7 fine-tuning dataset.
A converted **v2.1** version (per-episode parquet/mp4 layout, GR00T-trainable) is produced from this dataset using `scripts/lerobot_conversion/convert_v3_to_v2.py`.
---
## Dataset summary
| Item | Value |
| ------------------ | ---------------------------------------------- |
| Robot | OpenManipulator-X 4-DOF (`omx_follower`) |
| Codebase version | `v3.0` |
| Total episodes | 265 |
| Total frames | 144,788 |
| FPS | 30 |
| Cameras | `observation.images.top`, `observation.images.wrist` (640×480, 30 fps) |
| State / Action dim | 6 each (single_arm × 5, gripper × 1) |
| Action horizon | 16 (configured via `examples/OMX/omx_config.py`) |
| Tasks | 3 (see below) |
## Tasks (`task_index`)
| index | instruction |
| :---: | ------------------------------------------ |
| 0 | `pick up plate and place at target zone` |
| 1 | `pick up cup and place at target zone` |
| 2 | `return to home` |
### task distribution
| task_index | frames | pct |
| ---------- | -----: | ---: |
| 0 (plate) | 49,698 | 34.3% |
| 1 (cup) | 66,874 | 46.2% |
| 2 (home) | 28,216 | 19.5% |
---
## Scenario composition (Category A — Long Episode)
Every episode starts at home pose, performs the task(s), and returns to home.
Each scenario was collected as a separate sub-dataset (`serving_a1`…`serving_a7`) and then merged.
| sub-id | scenario | env state | success | retry | total |
| :----: | ---------------------- | -------------------- | ------: | ----: | ----: |
| A-1 | one cup | cup=1 | 30 | 5 | **35** |
| A-2 | one plate | plate=1 | 30 | 5 | **35** |
| A-3 | two plates | plate=2 | 30 | 10 | **40** |
| A-4 | two cups | cup=2 | 30 | 10 | **40** |
| A-5 | one plate + one cup | plate=1, cup=1 | 38 | 12 | **50** |
| A-6 | one plate + two cups | plate=1, cup=2 | 35 | 10 | **45** |
| A-7 | empty env (stop) | empty (2 sub-cases) | 18 | 0 | **18** |
| | **Total** | | **211** | **52**| **265**|
* **success**: completed normally.
* **retry**: gripper miss → short back-off → retry → success, all within a single episode.
* **A-7 sub-cases**: 7 episodes with both workspaces empty; 11 episodes with pick workspace empty and place workspace already containing previously-served objects (forces VLM to learn "stop when nothing to pick" even with clutter in the place zone).
---
## Label boundary scheme
Multi-task episodes are segmented into per-action task ranges so the policy can learn the language–action grounding.
| sub-id | segment pattern | # boundaries |
| :----: | ------------------------------------------------------------ | :----------: |
| A-1 | `cup(1) → home(2)` | 1 |
| A-2 | `plate(0) → home(2)` | 1 |
| A-3 | `plate(0) → plate(0) → home(2)` (per-plate restart) | 2 |
| A-4 | `cup(1) → cup(1) → home(2)` (per-cup restart) | 2 |
| A-5 | `cup(1) → plate(0) → home(2)` | 2 |
| A-6 | `cup(1) → cup(1) → plate(0) → home(2)` | 3 |
| A-7 | `home(2)` only | 0 |
Boundaries were auto-detected from gripper signal events (`fully_close` → next `start_open`) and audited manually for edge cases. Episodes that lacked the expected number of events had their missing boundary estimated from the median percentile of complete episodes within the same scenario.
---
## Repository layout
```
serving_a/
├── data/chunk-000/file-000.parquet # all frames, concatenated
├── meta/
│ ├── info.json # codebase_version, features, totals
│ ├── tasks.parquet # task_index → instruction
│ └── episodes/chunk-000/file-000.parquet # per-episode metadata + stats
└── videos/
├── observation.images.top/chunk-000/file-000.mp4
└── observation.images.wrist/chunk-000/file-000.mp4
```
---
## Companion dataset
This is **Category A only**. The matching **Category B (atomic-focused demos)** dataset is available as:
> [`jae0311/serving_b`](https://huggingface.co/datasets/jae0311/serving_b) — 160 episodes, 75,089 frames
For GR00T N1.7 fine-tuning we use a 2-stage schedule:
| Stage | Data | max-steps | LR | Goal |
| :---: | --------------- | :-------: | :----: | ----------------------------------------- |
| 1 | `serving_b` (B) | 5,000 | 1e-4 | atomic pick-and-place reliability |
| 2 | A+B merged | 25,000 | 5e-5 | full long-episode performance from Stage 1 ckpt |
---
## Citation / acknowledgments
- Model: [GR00T N1.7](https://huggingface.co/nvidia/GR00T-N1.7-3B) (NVIDIA)
- Dataset format: [LeRobot](https://github.com/huggingface/lerobot)
- Robot: ROBOTIS OpenManipulator-X
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