Add dataset card

README.md

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 ---
-license: gpl-3.0
+task_categories:
+  - robotics
+license: cc-by-sa-4.0
+tags:
+  - robotics
+  - lerobot
+  - so101
+  - so-101
+  - vision-language-action
+  - imitation-learning
+  - lerobot-dataset
 ---
+
+# SO-101 Desk Cleanup — 350 episodes
+
+Teleoperated SO-101 demonstrations of clearing objects from a desk into a bowl. The most open-ended task in Project-IRA: a variable number of arbitrary objects in arbitrary positions.
+
+Part of **[Project-IRA](https://huggingface.co/Project-IRA)** — Interactive Robotic Arm.
+Code: https://github.com/Project-IRA/interactive-robotic-arm
+
+| | |
+|---|---|
+| Episodes | **350 (250 in the merged training set)** |
+| Distinct task prompts | 35 |
+| LeRobot codebase version | `v3.0` |
+| Robot type | `so_follower` (SO-101, 6-DOF) |
+| Control frequency | 30 fps |
+| Language | English |
+
+## Composition
+
+350 episodes across 35 prompt phrasings, 10 episodes each.
+
+> [!IMPORTANT]
+> **Only 250 of these 350 episodes are in the merged training dataset.** Ten early
+> sessions (100 episodes, prompts listed under "Excluded from final training" below) used
+> a different coloured bowl and a messier scene, and were dropped for consistency.
+
+| Block | Setup | In merge? |
+|---|---|---|
+| V2/V3 early sessions | Different coloured bowl, messier scene | **No** |
+| One-or-multiple objects | 7–8 episodes with a single object, last 2–3 with several | Yes |
+| Two objects | Episodes 1–6 two objects, 7–8 one, 9–10 several | Yes |
+| Mixed counts | Episodes 1–3 one object, 4–7 two objects, 8–10 three to five | Yes |
+| Single-item variation | One specific item (marker / keys / lego brick / ball / dice) into a bowl whose position changes every 5 episodes | Yes |
+
+Objects used include markers, keys, lego bricks, a ball, and dice, cleared into a green
+bowl beside the arm.
+
+## Robot setup
+
+| | |
+|---|---|
+| Robot | SO-101 follower arm (6-DOF), `robot_type: so_follower` |
+| Teleoperation | SO-101 leader arm |
+| Control frequency | 30 fps |
+| State / action space | 6-dim: `shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`, `wrist_flex.pos`, `wrist_roll.pos`, `gripper.pos` |
+| Camera `observation.images.desk_view` | 800x600, h264 (recording) |
+| Camera `observation.images.wrist_left` | 640x480, h264 (recording) |
+
+> **Inference note:** both cameras are run at **640x480 during inference**, not at their
+> recording resolutions, to reduce the payload sent to the inference server.
+
+## Schema
+
+| Feature | dtype | shape |
+|---|---|---|
+| `observation.state` | float32 | (6,) — `shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`, `wrist_flex.pos`, `wrist_roll.pos`, `gripper.pos` |
+| `observation.images.desk_view` | video | (600, 800, 3), h264, 30 fps |
+| `observation.images.wrist_left` | video | (480, 640, 3), h264, 30 fps |
+| `action` | float32 | (6,) — same joint layout as state |
+| `timestamp`, `frame_index`, `episode_index`, `index`, `task_index` | — | bookkeeping |
+
+## Recording protocol
+
+- Recorded by teleoperating the SO-101 follower with an **SO-101 leader arm**.
+- **10 episodes per prompt.** The prompt phrasing was deliberately changed roughly every
+  10 episodes, so language conditioning sees many surface forms of the same intent.
+- Object positions and scene difficulty were varied systematically within each block
+  (e.g. early episodes with a single object, later ones with several).
+- **Recovery behaviour is incidental.** Where the operator made a mistake mid-episode and
+  corrected it, that correction stayed in the data. No recovery episodes were scripted
+  deliberately, so recovery coverage is uneven.
+
+## Prompts
+
+All in English, 10 episodes each.
+
+### Included in the merged training dataset (25 prompts, 250 episodes)
+
+1. `Put all objects from the desk into the bowl`
+2. `Clear the desk by placing all items into the green bowl`
+3. `Move everything on the desk into the bowl next to the arm`
+4. `Pick up all objects on the desk and drop them into the bowl`
+5. `Tidy up the desk by putting all items into the bowl to the right`
+6. `Place all items from the desk into the green bowl on the right side`
+7. `Remove all objects from the desk and put them in the bowl`
+8. `Clear the workspace by moving all objects into the nearby bowl`
+9. `Pick up each object on the desk and place it into the green bowl`
+10. `Put everything from the desk into the bowl directly to the right of the arm`
+11. `Sweep the desk clean by placing all items into the bowl`
+12. `Move all objects on the desk into the green bowl beside the robot`
+13. `Collect all items from the desk and deposit them into the bowl to the right`
+14. `Take each object off the desk and drop it into the green bowl next to the arm`
+15. `Clear the desk by picking up each object and placing it into the bowl nearby`
+16. `Grab every item on the desk and toss it into the green bowl`
+17. `The desk needs to be cleared, move all objects into the bowl`
+18. `Sort all desk items into the bowl sitting beside the robot arm`
+19. `Make the desk empty by relocating all items into the bowl next to you`
+20. `Transfer the objects from the desk surface into the green bowl nearby`
+21. `Take the marker and put it in the bowl`
+22. `Pick up the keys and put them in the bowl`
+23. `Grab a lego brick and drop it into the bowl`
+24. `Place the ball into the bowl to clear the desk.`
+25. `Clean the desk by putting the dice into the bowl`
+
+### Excluded from final training (10 prompts, 100 episodes)
+
+These are present in this dataset but **not** in
+[`Dataset_Full_Merged_Final_V1`](https://huggingface.co/datasets/Project-IRA/TPSoSe2026_Dataset_Full_Merged_Final_LeRobot_SO101_V1).
+
+1. `Clean up the desk`
+2. `Get the desk cleared off and everything into the bowl`
+3. `Round up all the objects on the desk and put them in the bowl`
+4. `Empty the desk completely by putting items in the green bowl`
+5. `Pick everything up off the desk and drop it in the bowl`
+6. `Clean the desk surface by moving items into the bowl next to it`
+7. `Put each item into the bowl until the desk is cleared`
+8. `Gather all the desk objects and place them in the bowl beside the arm`
+9. `Drop every object from the desk into the green bowl`
+10. `Place all desk items in the bowl so the desk is empty`
+
+## Usage
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+ds = LeRobotDataset("Project-IRA/TPSoSe2026_Dataset_Desk_Cleanup_LeRobot_SO101")
+print(ds.meta.info)
+```
+
+Train a policy on it:
+
+```bash
+lerobot-train \
+    --policy.path=lerobot/smolvla_base \
+    --dataset.repo_id=Project-IRA/TPSoSe2026_Dataset_Desk_Cleanup_LeRobot_SO101 \
+    --batch_size=64 --steps=200000 \
+    --policy.device=cuda
+```
+
+## Models trained on this dataset
+
+| Model | Quality |
+|---|---|
+| [SmolVLA V3 Desk Cleanup](https://huggingface.co/Project-IRA/TPSoSe2026_SmolVLA_LeRobot_SO101_Finetuning_V3_Desk_Cleanup) | Does not work |
+
+Single-task SmolVLA fine-tuning failed on this task. Desk cleanup only became reliable
+via multi-task Pi0.5 training on the merged dataset — see
+[Pi05 V7 Full V2](https://huggingface.co/Project-IRA/TPSoSe2026_Pi05_LeRobot_SO101_Finetuning_V7_Full_V2).
+
+This task is a strong instance of the **multimodal action distribution** problem: with
+several objects on the desk, any of them is a valid next target, and a policy that
+averages across those modes reaches for the empty space between them. Flow-matching
+action heads (used by both SmolVLA and Pi0.5) are chosen partly to address this.
+
+## Limitations
+
+- **Single environment.** One desk, one lighting setup, one camera geometry, one set of
+  physical objects. Policies trained here should not be expected to transfer.
+- **Teleoperated demonstrations** vary in quality and speed between operators and sessions.
+- **Recovery coverage is uneven** — see the recording protocol note above.
+- **No held-out split.** The dataset ships as a single `train` split; all episodes were
+  used for training. Evaluation was done by running policies on the physical arm.
+
+## Licensing
+
+Released under **CC BY-SA 4.0**. This is a share-alike licence: you may use, share and
+adapt this dataset, including commercially, provided you give attribution and release any
+derivative dataset under the same licence. It cannot be taken closed-source.
+
+Recorded with [LeRobot](https://github.com/huggingface/lerobot) (Apache-2.0); the LeRobot
+dataset format and tooling remain under their original licence.
+
+## Citation
+
+```bibtex
+@misc{project_ira_2026,
+  title        = {Project-IRA: Interactive Robotic Arm},
+  author       = {Baten, Cleo and Keppler, Bela and Sapper, Jonas},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/Project-IRA}},
+  note         = {Code: \url{https://github.com/Project-IRA/interactive-robotic-arm}}
+}
+```