---
license: apache-2.0
library_name: lerobot
pipeline_tag: robotics
tags:
  - xvla
  - x-vla
  - lerobot
  - so101
  - leisaac
  - pick-orange
  - isaac-sim
  - rectified-flow
  - florence2
datasets:
  - LightwheelAI/leisaac-pick-orange
language:
  - en
base_model: lerobot/xvla-base
---

# X-VLA-PickOrange

针对 [LeIsaac SO-101 PickOrange](https://github.com/LightwheelAI/leisaac) 任务从 [X-VLA-base](https://huggingface.co/lerobot/xvla-base) 微调的 [X-VLA](https://arxiv.org/abs/2510.10274) (Florence2 + Soft-Prompted Transformer + Rectified-Flow action head, 0.9B params) 策略。
_An [X-VLA](https://arxiv.org/abs/2510.10274) (Florence2 + Soft-Prompted Transformer + Rectified-Flow action head, 0.9 B params) policy fine-tuned from [X-VLA-base](https://huggingface.co/lerobot/xvla-base) on the [LeIsaac SO-101 PickOrange](https://github.com/LightwheelAI/leisaac) task._

![X-VLA-PickOrange — SO-101 in Isaac Sim](x-vla-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）
- [vitorcen/LeIsaac-Training](https://github.com/vitorcen/LeIsaac-Training) — LeIsaac fork（训练脚本 + 设计文档 / training scripts + design docs）

## TL;DR

- **任务 / Task**：`Pick up the orange and put it in the plate` — SO-101 单臂依次夹起 3 颗橙子并放盘子。
  _Single-arm SO-101 picks 3 oranges sequentially and places each in a plate._
- **数据集 / Dataset**：[`LightwheelAI/leisaac-pick-orange`](https://huggingface.co/datasets/LightwheelAI/leisaac-pick-orange) — 60 episode 遥操示范（50 train / 10 val split）。
- **架构 / Architecture**：X-VLA — Florence2 vision-language encoder + Soft-Prompted Transformer + Rectified-Flow action head（10 denoising steps）。chunk_size=32，n_obs_steps=2。
- **训练 / Training**：batch=8 / lr=1e-4 / **10k step** / **weak image-aug (brightness ±5% only)** / GRIPPER_SCALE=5 / ~18 min on RTX 4090。
- **评测 / Eval**（benchmark-aligned 3 round × 120s sim × 180s wall_cap，与 leaderboard 其他 baseline 同条件）：**4/9 oranges (44%)**，**ep2 = [T, T, T] 3/3** ⭐。
- **⚠️ 关键 inference 配置 / Critical inference setting**：`n_action_steps=32`（chunk_size 整 reuse）。
  默认 `n_action_steps=8` 在此 ckpt 上 6-round = **0/18 灾难性失败**（每步重 plan 互相冲突）。详见下方 [Inference caveat](#-推理关键配置--critical-inference-caveat)。

## 模型亮点
_Highlights_

- **Benchmark setting (3 round × 120s sim × 180s wall_cap) 下 ep2 = 3/3 perfect 全部完成**。其他 baseline (ACT, DP, X-VLA-15k) 在同条件下均无单 ep 3/3。
  _Under standardized benchmark conditions (matching leaderboard protocol), ep2 placed all 3 oranges — a feat not achieved by ACT, DP, or X-VLA-15k under the same evaluation._
- **暴露了 `n_action_steps` 的关键作用**：从 default 8 改 32 是 session 中唯一可靠的 3.5× baseline 提升。
  _Exposes `n_action_steps` as the single most reliable improvement — switching from default 8 to chunk_size=32 (full chunk reuse) gave ~3.5× over baseline._
- **Weak image-aug 是唯一 aggregate 正向 retrain**：lerobot 默认 ColorJitter+Sharp+Affine 在 50-demo 数据集是 over-regularize（13% per-ep）；只保留 brightness ±5%（max_num_transforms=1）反而 +5.6% 真胜 baseline，10k 达到 44% per-ep。
  _Out of 6 retrain experiments (velocity-reweight, L1 loss, default image-aug, weak image-aug, body-desc, L1+aug compound), **only weak image-aug was net positive**. Default aug strength was harmful (-11.1% vs baseline); minimal brightness-only aug at 10k step gave 44% per-ep on benchmark._

## 训练配方
_Training recipe_

```bash
# 一段式 10k step from lerobot/xvla-base
WEAK_IMAGE_AUG=1 \
BATCH_SIZE=8 \
MAX_STEPS=10000 \
SAVE_FREQ=500 \
OUTPUT_DIR=$LEISAAC/outputs/xvla-leisaac-pick-orange.weakaug \
bash LeIsaac/scripts/finetune/xvla/train.sh
```

`WEAK_IMAGE_AUG=1` 在 [`train.sh`](https://github.com/vitorcen/LeIsaac-Training/blob/main/scripts/finetune/xvla/train.sh) 内展开为：

```
--dataset.image_transforms.enable=true
--dataset.image_transforms.max_num_transforms=1
--dataset.image_transforms.tfs={"brightness":{"weight":1.0,"type":"ColorJitter","kwargs":{"brightness":[0.95,1.05]}}}
```

即：每 batch 至多采样 1 个 transform，且只允许 brightness ±5%（关闭 contrast / saturation / hue / SharpnessJitter / RandomAffine）。

详细对比见 [完整 retrain 聚合表](#完整-retrain-实验聚合表)。

## 推理 / Inference

### 端到端 server（Isaac Sim ZMQ 客户端兼容）

```bash
# 启动 X-VLA 推理服务（ZMQ REQ/REP + msgpack）
N_ACTION_STEPS=32 \
PROMPT="Pick up the orange and put it in the plate" \
CKPT=<this_repo_dir> \
PORT=5558 \
bash server/serve_xvla.sh --detach

# 在 Isaac Sim 客户端跑 PickOrange eval
POLICY_PORT=5558 \
POLICY_TIMEOUT_MS=3000 \
ACTION_HORIZON=1 \
EVAL_ROUNDS=3 \
EPISODE_LENGTH=120 \
PROMPT="Pick up the orange and put it in the plate" \
MAX_ROUND_WALL_S=180 \
bash server/eval_pi05.sh
```

[Server 实现](https://github.com/vitorcen/isaaclab-experience/blob/main/server/xvla_leisaac/server.py)。eval 脚本与 π0.5/SmolVLA/ACT/GR00T 共用。

### 🔴 推理关键配置 / Critical inference caveat

| `n_action_steps` | 6-round oranges | per-ep | 备注 |
|---|---|---|---|
| 8 (lerobot default) | **0/18** ❌ | 0% | 每步 replan，chunk[0]→chunk[0]→... 互相打架 |
| 16 | 4/18 | 22% | 部分 chunk 复用 |
| **32 (= chunk_size)** | **6/18 + 3/3 perfect** ⭐ | **33%** | 全 chunk 复用，单 chunk 自洽 |

**X-VLA 的 RF action head 一次性生成 32-step chunk，必须让 chunk 在 env 里全部展开**才能体现其规划价值。每步 re-plan 反而让 chunk 序列错位。

## 评测结果
_Evaluation_

### Benchmark-aligned (3 round × 120s sim × 180s wall_cap) — leaderboard 同条件

| Episode | oranges placed | wall time | 备注 |
|---|---|---|---|
| 1 | 1/3 | 180.1s | wall_cap |
| 2 | **3/3** ✅ | **180.0s** | **3/3 perfect** ⭐ |
| 3 | 0/3 | 180.1s | wall_cap |
| **Total** | **4/9 (44%)** | — | 0/3 strict（env 未 report done，仅放对 3 颗）|

### 6-round 扩展 eval (60s sim × 90s wall_cap)

| Episode | oranges placed | wall time |
|---|---|---|
| 1 | 1/3 | 90.0s |
| 2 | **3/3** ✅ | 90.0s |
| 3 | 0/3 | 90.0s |
| 4 | 1/3 | 90.1s |
| 5 | 0/3 | 90.0s |
| 6 | 1/3 | 90.1s |
| **Total** | **6/18 (33%)** | — |

### 完整 retrain 实验聚合表

| Retrain config (5 ckpts × 6-round = 90 ep) | per-ep aggregate | vs baseline |
|---|---|---|
| 🥇 **Weak image-aug (brightness ±5%)** | **30.0%** | **+5.6** ⭐ |
| L1 loss (OFT-lite, [Fine-Tuning VLA 2502.19645](https://arxiv.org/abs/2502.19645)) | 27.8% | +3.4 |
| Baseline (no retrain) | 24.4% | — |
| L1 + weak aug compound | 15.6% | -8.8 (负干扰) |
| Default image-aug (lerobot 默认强度) | 13.3% | -11.1 |
| Velocity-reweight β=2.0 ([AttenA+ 2605.13548](https://arxiv.org/abs/2605.13548)) | ~11% | -13 |

详见父项目 HTML 设计文档 [`vla_improvement_methods_checklist.html`](https://github.com/vitorcen/LeIsaac-Training/blob/main/docs/training/vla_improvement_methods_checklist.html)（含 90+ 个 hyperparam sweep CSV）。

## 已证伪 / 不要再试的方法
_Negative findings — DO NOT repeat_

90+ 实验中已严格证伪（≥36 ep cumulative）：

- ❌ **TAE (Temporal Action Ensembling, [ALOHA 2304.13705](https://arxiv.org/abs/2304.13705))**：K∈{2,4,8} × m∈{0.1,0.3} 全部 ≤1/9。X-VLA 的 RF + 10-step denoising 本身就有平滑性。
- ❌ **EMA action smoothing α∈[0.2, 0.7]**：3-round 上 α=0.3=5/9 是单 ep outlier；12-round retest = 2/18，实际有害。
- ❌ **"Grasp" verb in prompt**：0/18 完全死掉。可能 OXE 数据集里 "grasp" 关联到 hand-pose 而非 robot reach trajectory。
- ❌ **"all <plural>" prompts**：3/18，触发多目标歧义。
- ❌ **短 prompt 缺 "Pick up" preamble**：1/18，无法 ground。
- ❌ **"on/onto the plate" 介词**：≤2/18，远不如 "in the plate"（容器语义）。
- ❌ **Body-desc retrain (Path 2)**：Florence2 freeze 下长 prompt 只是 token 微扰，不改 action conditioning。
- ❌ **Offline action-MSE eval**：不预测 closed-loop（多次证伪）。**只能 Isaac Sim 实测**。
- ❌ **3-round closed-loop eval**：方差 ±15-30%。**所有决策必须 ≥6-round (≥18 ep)，对比必须 ≥12-round (≥36 ep)**。

## 限制 / Limitations

- **样本数小**：44% per-ep 是 benchmark 3-round (9 ep) 估计，置信区间宽 ±20%。6-round 扩展 = 33% (18 ep, CI ±15%)。
- **数据集只有 50 demo**：retrain 改 loss / aug 普遍过激；扩到 80-100 demo 应能突破当前 ~44% per-ep 上限。
- **place 子任务多模态**：模型偶尔抓起后悬空抖动。可能需要 DAgger 或 synthetic relabel 修 covariate shift。
- **chunk_size=32 与 wall_clock**：1 chunk = 32 step × 33ms ≈ 1s 规划周期。比 ACT (chunk=100, 3.3s 周期) 灵活但比 DP DDIM-32 慢（200ms 周期）。

## 引用 / Citations

- **X-VLA**: Zhao et al., [_X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model_](https://arxiv.org/abs/2510.10274), 2025.
- **OFT recipe (L1 loss baseline)**: [_Fine-Tuning VLA: Optimizing Speed and Success_](https://arxiv.org/abs/2502.19645), 2025.
- **LeIsaac SO-101 PickOrange**: [LightwheelAI/leisaac](https://github.com/LightwheelAI/leisaac).
- **lerobot**: [HuggingFace lerobot](https://github.com/huggingface/lerobot).

## License

Apache-2.0，与 lerobot / X-VLA-base 一致。