---
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