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README.md

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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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meta/info.json

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+{
+    "codebase_version": "v3.0",
+    "fps": 30,
+    "features": {
+        "action": {
+            "dtype": "float32",
+            "names": [
+                "shoulder_pan.pos",
+                "shoulder_lift.pos",
+                "elbow_flex.pos",
+                "wrist_flex.pos",
+                "wrist_roll.pos",
+                "gripper.pos"
+            ],
+            "shape": [
+                6
+            ]
+        },
+        "observation.state": {
+            "dtype": "float32",
+            "names": [
+                "shoulder_pan.pos",
+                "shoulder_lift.pos",
+                "elbow_flex.pos",
+                "wrist_flex.pos",
+                "wrist_roll.pos",
+                "gripper.pos"
+            ],
+            "shape": [
+                6
+            ]
+        },
+        "observation.images.top": {
+            "dtype": "video",
+            "shape": [
+                480,
+                640,
+                3
+            ],
+            "names": [
+                "height",
+                "width",
+                "channels"
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+                "video.fps": 30,
+                "video.channels": 3,
+                "has_audio": false
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+        "observation.images.wrist": {
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+            "shape": [
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+                640,
+                3
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+        "task_index": {
+            "dtype": "int64",
+            "shape": [
+                1
+            ],
+            "names": null
+        }
+    },
+    "total_episodes": 265,
+    "total_frames": 144788,
+    "total_tasks": 3,
+    "chunks_size": 1000,
+    "data_files_size_in_mb": 100,
+    "video_files_size_in_mb": 200,
+    "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
+    "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
+    "robot_type": "omx_follower",
+    "splits": {
+        "train": "0:265"
+    }
+}

meta/tasks.parquet

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