---
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}}
}
```