Instructions to use tonghuiwang123/so100-smolvla-s1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use tonghuiwang123/so100-smolvla-s1000 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
File size: 1,668 Bytes
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library_name: lerobot
tags: [robotics, lerobot, so100, so-arm100]
---
# so100-smolvla-s1000
LeRobot policy for **SO-ARM100 / SO100**, task: `Grab the white cube to the white cup`
Trained on `tonghuiwang123/test10` (2 cameras @1280x720, 30fps).
## 相机对照(顺序不能错,错了不报错但表现会莫名变差)
| 物理位置 | 设备 | 本模型要求的 key |
|---|---|---|
| 顶部 (top-down) | `/dev/video2` | `camera1` |
| 腕部 (wrist) | `/dev/video4` | `camera2` |
> 建议把 `/dev/videoN` 换成 `/dev/v4l/by-id/...` 的稳定路径,
> 因为 videoN 编号在重插 USB / 重启后会漂移。
## 真机部署
```bash
lerobot-record \
--robot.type=so100_follower --robot.port=/dev/ttyACM0 \
--robot.id=my_awesome_follower_arm \
--robot.cameras="{
camera1: {type: opencv, index_or_path: /dev/video2, width: 1280, height: 720, fps: 30},
camera2: {type: opencv, index_or_path: /dev/video4, width: 1280, height: 720, fps: 30},
}" \
--dataset.repo_id=tonghuiwang123/eval_so100-smolvla-s1000 \
--dataset.single_task="Grab the white cube to the white cup" \
--policy.path=tonghuiwang123/so100-smolvla-s1000
```
分辨率必须 1280x720,与训练数据一致。
## 推理开销
| 模型族 | 显存 | 单次推理 |
|---|---|---|
| ACT | 0.70 GB | ~56 ms (720p x2) |
| SmolVLA | 1.03 GB | - |
| Pi0 | 9.43 GB | - |
| Pi0.5 | 9.90 GB | - |
**ACT 注意**:默认 `n_action_steps=100` 会开环执行 3.3 秒,期间腕部相机画面全部被丢弃。
若出现「朝目标动但抓不准」,优先调小到 15(约 0.5 秒闭环一次),无需重训。
|