---
license: apache-2.0
library_name: lerobot
pipeline_tag: robotics
tags:
  - smolvla
  - lerobot
  - so101
  - leisaac
  - pick-orange
  - isaac-sim
datasets:
  - LightwheelAI/leisaac-pick-orange
language:
  - en
base_model: lerobot/smolvla_base
---

# SmolVLA-PickOrange

针对 [LeIsaac SO-101 PickOrange](https://github.com/LightwheelAI/leisaac) 任务 LoRA-free 微调的 [SmolVLA](https://huggingface.co/lerobot/smolvla_base) 策略 — 自训 **15k step**（main，sweep best）。
_A fine-tuned [SmolVLA](https://huggingface.co/lerobot/smolvla_base) policy on the [LeIsaac SO-101 PickOrange](https://github.com/LightwheelAI/leisaac) task. `main` = **step-15000 (sweep best)**, full-parameter from `lerobot/smolvla_base`._

![SmolVLA-PickOrange — SO-101 in Isaac Sim](smolvla-pick-orange.jpg)

**🔗 项目仓库 / Project repos**：
- [vitorcen/isaaclab-experience](https://github.com/vitorcen/isaaclab-experience) — Isaac Lab + LeIsaac 多策略横评（parent project）— 含 7-baseline benchmark
- [vitorcen/LeIsaac-Training](https://github.com/vitorcen/LeIsaac-Training) — LeIsaac fork（训练脚本 + 设计文档 / training scripts + design docs）

> **关于命名 / About the name**：`config.type=smolvla` (LeRobot v1 SmolVLA implementation)，backbone 用 `HuggingFaceTB/SmolVLM2-500M-Video-Instruct` (SmolVLM**2**)。LeRobot 自己也叫 `smolvla` 而不是 `smolvla2`，所以仓库名沿用 `SmolVLA-PickOrange`。
> _`config.type=smolvla` (LeRobot v1 SmolVLA implementation) with `HuggingFaceTB/SmolVLM2-500M-Video-Instruct` backbone. LeRobot keeps the policy name `smolvla` (matching their naming), so this repo follows suit._

## TL;DR

- **任务 / Task**：`Pick up the orange and place it on the plate` — SO-101 单臂依次夹起 3 颗橙子并放盘子。
- **数据集 / Dataset**：[`LightwheelAI/leisaac-pick-orange`](https://huggingface.co/datasets/LightwheelAI/leisaac-pick-orange) — 60 episode 遥操示范，30 fps，dual-cam 480×640。
- **架构 / Architecture**：SmolVLA v1（450M），SmolVLM2-500M-Video-Instruct backbone + Action Expert，`chunk_size=50`。
- **训练 / Training**：full-param 微调（无 LoRA），batch=8 / lr=1e-4 / 总 30k step 训练，30k 后明显过拟合。**main = step-15000 (sweep best)**。
- **评测 / Eval**（Isaac Sim 5.1，**5 round × 3 颗 = 15 颗**，post-fix placement check）：
  - **2/5 strict rounds, 8/15 oranges (53%), 133s avg** ← 15k @ h=50
  - 详见 [`vitorcen/isaaclab-experience`](https://github.com/vitorcen/isaaclab-experience) README leaderboard

## Checkpoint branches / ckpt 分支

| Branch | Step | env rounds | oranges | avg s | 备注 |
|---|---|---|---|---|---|
| **`main`** | **15000** | **2/5** | **8/15 (53%)** | **133s** | sweep best ⭐ |
| `ckpt-20k` | 20000 | 0/5 | 6/15 (40%) | 180s | (will be uploaded if needed) |
| `ckpt-25k` | 25000 | 1/5 | 5/15 (33%) | 160s | (will be uploaded if needed) |
| `ckpt-30k` | 30000 | 0/5 | 4/15 (27%) | 180s | overfit; 旧 main 已搬到此分支 |

_Sweep 用 h=50 (= train chunk_size), 5 round × 5 ckpt = 75 ep on Isaac Sim 5.1，单一 RTX 4090。_

## Ckpt sweep 曲线 / Ckpt sweep curve

15k 是最佳点：训得久了开始 overfit 60 ep 这个小数据集，过早（10k 以下）尚未学到完整 pick-place-pick-place 长程序列。

```
oranges/15
  9 |
  8 |          ⭐ 15k
  7 |
  6 |       ●  20k
  5 |          ●  25k
  4 |             ●  30k
  3 |
  2 |
  1 |●  10k
  0 +----------------------
    10  15  20  25  30  k step
```

## 推理 inference 配置

```bash
# 1. 启 LeRobot async policy server
bash server/start_server.sh --lerobot-only

# 2. 跑 LeIsaac PickOrange eval
POLICY_CHECKPOINT=wsagi/SmolVLA-PickOrange \
ACTION_HORIZON=50 \
EVAL_ROUNDS=5 EPISODE_LENGTH=120 MAX_ROUND_WALL_S=180 \
PROMPT="Pick up the orange and put it in the plate" \
conda run -n isaaclab python LeIsaac/scripts/evaluation/policy_inference.py \
    --task=LeIsaac-SO101-PickOrange-v0 \
    --policy_type=lerobot-smolvla \
    --policy_port=8080 \
    --policy_checkpoint_path=$POLICY_CHECKPOINT \
    --policy_action_horizon=$ACTION_HORIZON \
    --eval_rounds=$EVAL_ROUNDS --episode_length_s=$EPISODE_LENGTH \
    --max_round_wall_s=$MAX_ROUND_WALL_S \
    --policy_language_instruction="$PROMPT" \
    --device=cuda --enable_cameras
```

**关键 inference 参数 (per [scripts/benchmark/baselines_action_horizon.tsv](https://github.com/vitorcen/isaaclab-experience/blob/main/scripts/benchmark/baselines_action_horizon.tsv))**：
- `action_horizon=50`（= train chunk_size，h=40 实测略弱）
- 选 branch `main` 拿 best；或 `ckpt-30k` / 任何 `ckpt-Nk` 拿对应阶段。

## 训练配方
_Training recipe_

| 项 / Item | 值 / Value |
|---|---|
| Dataset | `LightwheelAI/leisaac-pick-orange` (60 ep, dual-cam 480×640 RGB + 6 DOF state, 30 Hz) |
| Policy | `smolvla` (LeRobot 实现) |
| Backbone | `HuggingFaceTB/SmolVLM2-500M-Video-Instruct` + Action Expert |
| `chunk_size` / `n_action_steps` | 50 / 50 |
| Batch size | 8 (full-param, no LoRA) |
| Optimizer | AdamW, lr=1e-4 |
| Steps | 30000 (~14h on 4090) → main = **15000** (sweep best) |
| `video_backend` | `pyav`（torchcodec 长跑 segfault） |
| Image augmentation | 无 |
| Train expert only | False（全参数） |

> **🚨 schema-free base 关键 fix**：训练前必须用 [`prepare_base.sh`](https://github.com/vitorcen/LeIsaac-Training/blob/main/scripts/training/smolvla/prepare_base.sh) 剥光 `lerobot/smolvla_base` 自带的 `input_features` / `empty_cameras`（默认 `camera1/2/3 @ 256×256` 会污染微调路径），否则训练时 schema 不对齐 → forward 报 KeyError 或 silent 训坏。

## Eval 历史 / Eval history

| 版本 | env rounds | oranges | avg s | 备注 |
|---|---|---|---|---|
| 30k h=50 (旧 leaderboard) | 1/3 | 5/9 (55%) | 355s | sticky-OR + 3-round（旧 buggy 计数） |
| **30k h=50 (post-fix 5-round)** | 0/5 | 4/15 (27%) | 180s | 真实 5-round + pre-step snapshot |
| **15k h=50 (post-fix 5-round)** | **2/5** | **8/15 (53%)** | **133s** | **sweep best, 现 main** ⭐ |

## License

Apache-2.0（继承自 `lerobot/smolvla_base`）。