---
task_categories:
  - robotics
license: cc-by-sa-4.0
tags:
  - robotics
  - lerobot
  - so101
  - so-101
  - vision-language-action
  - imitation-learning
  - lerobot-dataset
---

# SO-101 Fetch Ball — 100 episodes

Teleoperated SO-101 demonstrations of picking up a ball and placing it into a waiting human hand. The smallest and most recently recorded task dataset in Project-IRA.

Part of **[Project-IRA](https://huggingface.co/Project-IRA)** — Interactive Robotic Arm.
Code: https://github.com/Project-IRA/interactive-robotic-arm

| | |
|---|---|
| Episodes | **100** |
| Distinct task prompts | 10 |
| LeRobot codebase version | `v3.0` |
| Robot type | `so_follower` (SO-101, 6-DOF) |
| Control frequency | 30 fps |
| Language | English |

## Composition

100 episodes across 10 prompt phrasings, 10 episodes each.

Recording scheme: a human holds their hand in one position while the ball is placed on
the table; the arm picks up the ball and places it into the waiting hand. The hand
position is held constant for 5 episodes at a time, then moved.

This is the only task in Project-IRA involving **direct human-robot handover**, which
means a human hand is present in the camera frames throughout.

## 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 10 prompts, in English, 10 episodes each.

1. `Fetch the ball and put it into my hand`
2. `Bring the ball back into my hand`
3. `Retrieve the ball and drop it into the waiting hand`
4. `Put the ball into the open hand`
5. `Carry the ball and set it down in the waiting palm`
6. `Pass the ball to the waiting hand`
7. `Fetch the ball`
8. `Find the ball on the desk and put it in the hand`
9. `Grab the multicolored ball and then drop it gently into the hand`
10. `Pick up the round ball and place it into the hand`

## Usage

```python
from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("Project-IRA/TPSoSe2026_Dataset_Fetch_Ball_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_Fetch_Ball_LeRobot_SO101 \
    --batch_size=64 --steps=200000 \
    --policy.device=cuda
```

## Models trained on this dataset

No single-task model was trained on this dataset. Ball fetching is covered by the
multi-task models trained on
[`Dataset_Full_Merged_Final_V1`](https://huggingface.co/datasets/Project-IRA/TPSoSe2026_Dataset_Full_Merged_Final_LeRobot_SO101_V1) —
best results from
[Pi05 V7 Full V2](https://huggingface.co/Project-IRA/TPSoSe2026_Pi05_LeRobot_SO101_Finetuning_V7_Full_V2).

> [!NOTE]
> Because this task involves handing an object to a person, human hands appear in the
> training frames. If you fine-tune on this data, be aware the policy will move toward a
> human hand in the workspace by design.

## 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}}
}
```