---
license: apache-2.0
base_model: lerobot/smolvla_robotwin
tags:
- robotics
- vla
- smolvla
- lerobot
- robotwin
- imitation-learning
pipeline_tag: robotics
library_name: lerobot
---

# SmolVLA RoboTwin `place_bread_skillet` (50 ep, single instruction)

SmolVLA policy fine-tuned on 50 demonstration episodes of the **`place_bread_skillet`** task from **RoboTwin 2.0** (`demo_clean` config), starting from the [`lerobot/smolvla_robotwin`](https://huggingface.co/lerobot/smolvla_robotwin) base checkpoint.

## Task

Dual-arm pick-and-place: pick up the bread and place it inside the skillet.

- **Robot**: Agilex dual-arm, end-effector control (16D state, 16D action)
- **Cameras**: 3 RGB streams — `dual_cam_global`, `cam_wrist_65`, `cam_wrist_75`
- **Control rate**: 10 Hz
- **Single fixed instruction**: `"place the bread in the skillet"` (Strategy A, not random per-episode)

## Training

| Config | Value |
|---|---|
| Base checkpoint | `lerobot/smolvla_robotwin` |
| Training data | 50 RoboTwin demonstrations (subset of `place_bread_skillet_300ep`), strategy A single instruction |
| Frames | 8,298 (~165 frames/ep) |
| Batch size | 32 |
| Steps | 6000 (~23 epochs) |
| Optimizer | AdamW, lr=1e-4 |
| Scheduler | Cosine, warmup=300, decay=6000 |
| Chunk size | 50 |
| Final train loss | 0.008 |
| Walltime | ~2h 15min (A100) |

## Evaluation

Evaluated in RoboTwin 2.0 simulator (`demo_clean` config), 10 episodes, `max_steps=400`, `action_chunk_exec=50`.

| Model | Data | Base | Success |
|---|---|---|---|
| SmolVLA (`smolvla_base`) | 300 ep | smolvla_base | **0/10 (0%)** |
| **SmolVLA (this model)** | **50 ep** | **smolvla_robotwin** | **6/10 (60%)** |
| X-VLA (`xvla-base`) | 300 ep | xvla-base | 8/10 (80%) |

Training with the `smolvla_robotwin` base checkpoint enables strong data efficiency: with 6× less data and 3× fewer training steps, this model jumps from 0% to 60% success rate on this task.

Successful episodes complete in 132–201 environment steps (13–20s); failed episodes time out at 400 steps.

## Usage

```python
from lerobot.policies.smolvla import SmolVLAPolicy

policy = SmolVLAPolicy.from_pretrained("arrow-hf/smolvla-robotwin-place-bread-skillet-50ep")
```

See [LeRobot documentation](https://huggingface.co/docs/lerobot) for inference setup.

## Citation

Built on [SmolVLA](https://huggingface.co/lerobot/smolvla_base) and [SmolVLA-RoboTwin](https://huggingface.co/lerobot/smolvla_robotwin), fine-tuned on data collected from [RoboTwin 2.0](https://github.com/TianxingChen/RoboTwin).