---
license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - so101
  - so-arm101
  - real-robot
  - teleoperation
  - imitation-learning
  - manipulation
  - human-robot-handover
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files: data/*/*.parquet
---

# SO-ARM101 — "Hand me the blue napkin"

Teleoperated demonstrations of a **human–robot handover** on a real
[SO-ARM101](https://github.com/TheRobotStudio/SO-ARM100) — a 6-DOF, ~$300 open-source arm with
STS3215 servos. The robot picks up a pack of blue tissues from the table and places it into a
human hand.

Recorded with [LeRobot](https://github.com/huggingface/lerobot) (`codebase_version: v2.1`).

| | |
|---|---|
| Robot | `so101_follower`, 6 DOF |
| Task | `"Hand me the blue napkin"` (single task) |
| Episodes | **101** (complete set) |
| Frames | 40,493 |
| Episode length | 400–401 frames ≈ **13.4 s** each (very consistent) |
| Frame rate | 30 fps |
| Cameras | 2 × 480×640 RGB, AV1-encoded MP4 |
| Total duration | ≈ 22.5 minutes |
| Collection | human teleoperation (leader–follower) |

This is the **full set the SmolVLA policy was trained on** — an earlier release contained only a
43-episode subset.

## Trained policies

| | |
|---|---|
| [`Twu31/smolvla-so101-blue-napkin-160k`](https://huggingface.co/Twu31/smolvla-so101-blue-napkin-160k) | SmolVLA, 160k steps — **80%+ real-arm success** (production) |
| [`Twu31/smolvla-so101-blue-napkin-50k`](https://huggingface.co/Twu31/smolvla-so101-blue-napkin-50k) | earlier 50k-step checkpoint |
| [`Twu31/so101_hand_blue_napkin_eval_rollouts`](https://huggingface.co/datasets/Twu31/so101_hand_blue_napkin_eval_rollouts) | recorded real-arm eval rollouts from both |

## Features

| Key | Type | Shape | Contents |
|---|---|---|---|
| `observation.state` | float32 | (6,) | `shoulder_pan.pos`, `shoulder_lift.pos`, `elbow_flex.pos`, `wrist_flex.pos`, `wrist_roll.pos`, `gripper.pos` |
| `action` | float32 | (6,) | same six joint targets, as commanded by the teleoperator |
| `observation.images.front` | video | (480, 640, 3) | fixed front-facing camera |
| `observation.images.handeye` | video | (480, 640, 3) | wrist-mounted (hand-eye) camera |
| `timestamp`, `frame_index`, `episode_index`, `index`, `task_index` | — | — | standard LeRobot indexing |

Joint positions are in the SO-ARM101 servo's native normalised position units, not radians.

## Usage

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

ds = LeRobotDataset("Twu31/so101_hand_blue_napkin")
print(ds.num_episodes, ds.num_frames)     # 101 40493

sample = ds[0]
sample["observation.state"]               # (6,)
sample["action"]                          # (6,)
sample["observation.images.front"]        # (3, 480, 640)
sample["observation.images.handeye"]      # (3, 480, 640)
sample["task"]                            # "Hand me the blue napkin"
```

Or just the tabular part, without decoding video:

```python
from datasets import load_dataset
ds = load_dataset("Twu31/so101_hand_blue_napkin")
```

Action chunking works well here: at 30 fps a 50-step chunk covers 1.7 s, and episodes are
uniform enough that chunk boundaries land in comparable phases of the motion.

## What this data has been used for

Two projects, both documented at
**https://github.com/twu3202/soarm101-offline-rl-experiments**:

1. **A working SmolVLA policy.** A SmolVLA v2 policy (SmolVLM2-500M backbone + flow-matching
   action expert) was fine-tuned on this dataset and reached **80%+ success on the real arm** —
   101 demos, 160k steps. The checkpoint is published at
   [`Twu31/smolvla-so101-blue-napkin-160k`](https://huggingface.co/Twu31/smolvla-so101-blue-napkin-160k).
2. **A negative result on offline RL.** Five offline-RL approaches (IQL with several reward
   labellings, Vision-IQL on frozen SigLIP features, and Residual IQL on top of SmolVLA) were
   tried on these demonstrations to push past the IL baseline. The best variant improved
   held-out action MSE by 51% over SmolVLA alone (0.0039 vs 0.0087) and then scored **0% on
   the real arm** — per-frame residuals break SmolVLA's chunk-level self-correction and the
   closed-loop distribution shift cascades.

   The transferable lesson for anyone using this dataset: **open-loop action MSE on held-out
   demonstrations does not predict closed-loop success.** Evaluate in the loop.

## Structure

```
data/chunk-000/episode_0000{00..100}.parquet    # 6D state + 6D action + indices, 101 files
videos/chunk-000/observation.images.front/      # 101 × MP4 (AV1)
videos/chunk-000/observation.images.handeye/    # 101 × MP4 (AV1)
meta/info.json                                  # feature schema, fps, counts
meta/episodes.jsonl                             # per-episode length and task
meta/episodes_stats.jsonl                       # per-episode per-feature min/max/mean/std
meta/tasks.jsonl                                # the single task string
```

## Limitations

- **One task, one scene, one operator.** Fixed camera placement, fixed table, the same tissue
  pack throughout. Do not expect a policy trained on this to generalise to other objects,
  backgrounds, or receiving hands.
- **101 episodes** is still small in absolute terms, and there is no held-out split defined —
  `splits` declares all 101 as `train`. If you need a validation set, carve one out yourself.
- **Successful demonstrations only.** There are no failure episodes, no recovery behaviour, and
  no reward or success annotations. Anything reward-shaped has to be derived (the project above
  did this from gripper events and joint-velocity-based success-frame detection).
- **AV1 video.** Decoding needs a recent `av`/`ffmpeg`; older installs will fail to read frames.
- The human hand's position varies between episodes, so the same arm state can map to different
  correct actions. This multi-modality is what made state-only IQL degenerate to behaviour
  cloning in the experiments above — worth knowing before fitting a unimodal policy.

## Privacy

A human hand and forearm appear in the frames of both cameras. No faces, no identifying
features, no audio.

## Citation

```bibtex
@dataset{so101_hand_blue_napkin_2026,
  author    = {Twu31},
  title     = {SO-ARM101 — "Hand me the blue napkin": teleoperated handover demonstrations},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/Twu31/so101_hand_blue_napkin}
}
```

Recorded with [LeRobot](https://github.com/huggingface/lerobot) on
[SO-ARM101](https://github.com/TheRobotStudio/SO-ARM100) hardware.