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README.md

@@ -0,0 +1,149 @@
+---
+license: apache-2.0
+library_name: lerobot
+pipeline_tag: robotics
+tags:
+  - pi05
+  - openpi
+  - lerobot
+  - so101
+  - leisaac
+  - pick-orange
+  - isaac-sim
+  - flow-matching
+  - vla
+  - negative-result
+datasets:
+  - LightwheelAI/leisaac-pick-orange
+language:
+  - en
+---
+
+# Pi0.5-PickOrange — π0.5 PyTorch expert-only FT (⚠️ negative result)
+
+**⚠️ 这是一个有据可查的失败实验（已公开作为反面教材 / educational negative result）**：
+20-round strict benchmark = **1/60 oranges (1.7%)**，在 [STRICT_LEADERBOARD](https://github.com/vitorcen/isaaclab-experience/blob/main/scripts/benchmark/STRICT_LEADERBOARD.md) 上末位，**比同任务的 SmolVLA 低 15 倍**。发布的目的是把"为什么 π0.5 在 LeIsaac PickOrange 上学不会"这件事用 ckpt 本身固定下来，供后续研究者复现 / 否证。
+
+_This is a **deliberately published failure** — a documented negative result. 20-round strict eval = 1/60 oranges (1.7%), last place on the strict leaderboard, **15× worse than SmolVLA** on the same task. Published to anchor the "why π0.5 doesn't learn this task" claim with a real checkpoint, so others can reproduce / refute._
+
+**🔗 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）
+- 完整 negative report HTML: [`pi05_pytorch_expert_ft_negative.html`](https://github.com/vitorcen/LeIsaac-Training/blob/main/docs/training/pi05_pytorch_expert_ft_negative.html)
+
+## TL;DR
+
+| Item | Value |
+|------|-------|
+| **任务 / Task** | SO-101 PickOrange — 单臂依次夹起 3 颗橙子放盘子 |
+| **数据集 / Dataset** | [`LightwheelAI/leisaac-pick-orange`](https://huggingface.co/datasets/LightwheelAI/leisaac-pick-orange) (60 demos, 30Hz) |
+| **架构 / Architecture** | π0.5 = PaliGemma-2B VLM (frozen) + Gemma-300M action expert (trainable) + flow-matching |
+| **可训参数 / Trainable params** | 693M (gemma_expert layers 425M + lm_head 263M + norm 3M) |
+| **配方 / Recipe** | `train_expert_only=true`, `freeze_vision_encoder=true`, bf16, lr=2.5e-5, chunk=50, batch=1 + grad_accum=8, 10k steps |
+| **vision input** | **SigLIP @ 224×224**（PaliGemma 硬编码，**主嫌**） |
+| **Strict benchmark** | **1/60 oranges (1.7%)** — 20 rounds × 3 ep × 1 orange/ep, ckpt-2000 |
+| **σ(5-round)** | 0.50 / 15 (3.3%) — worst-case (μ-1σ) = **-0.25 / 15** |
+| **Leaderboard 排名 / Rank** | **6/6（末位）**，低 SmolVLA 15× |
+| **Inference latency** | ~108 ms / chunk (50-step flow matching, RTX 4090) |
+| **GPU hours** | ~3.5 h on RTX Pro 6000 (bf16, ZeRO-2 offload) |
+
+## 为什么发布失败模型 / Why publish a failed model
+
+科研里负面结果通常被丢进抽屉，但其实和成功一样有价值：
+
+1. **锁定假设**：让后续研究者可以 load 这个 ckpt 直接验证"是不是这套配方在这个数据集上真的不行"，避免反复踩同样的坑。
+2. **隔离变量**：训练侧的 dataloader / preprocessor / postprocessor / camera mapping / freeze 配置都已经调通（基础设施 4 个 bug 修完），失败不是 infra 噪声，而是**架构 vs 任务**的真实信号。
+3. **量化"偶尔的 1 只"**：用户最初看到 3-round 跑出 2/9 觉得有希望，但 20-round 1/60 证明那只是 Bernoulli outlier (p≈1.7%)。
+
+_Negative results matter as much as positive ones. This ckpt lets others verify the failure mode without re-spending the GPU hours._
+
+## 根因分析（主嫌 80%）/ Root cause (main suspect, 80% confidence)
+
+**PaliGemma-2B 的 SigLIP vision encoder 硬编码 224×224 输入**，而 LeIsaac 原生 640×480 → 2.86× downscale 后橙子只剩 **10–17 px**，**≤1 个 SigLIP patch (14px)**。
+
+对比同任务上 work 的模型：
+
+| Model | Vision encoder | Input res | Orange size after resize | Result |
+|-------|---------------|-----------|--------------------------|--------|
+| GR00T-N1.7 | Eagle-2 ViT | 448 | 22-34 px (1.5–2.4 patch) | 68.3% ✅ |
+| SmolVLA | SigLIP | 512 | 24-40 px (1.7–2.9 patch) | 25.0% ✅ |
+| **π0.5 (this)** | **SigLIP** | **224** | **10-17 px (≤1 patch)** | **1.7% ❌** |
+
+→ 橙子在 vision token 上几乎不可见，"freeze 整个 PaliGemma + 只训 action expert"再多 token 也无法补救 vision bottleneck。
+
+_PaliGemma's SigLIP is hardcoded to 224×224 — after downscaling LeIsaac's native 640×480, oranges shrink to ≤1 SigLIP patch. No amount of expert-only training can recover information already lost at the vision encoder._
+
+## 训练配方 / Training recipe
+
+```bash
+# 训练入口 / training entry
+bash LeIsaac/scripts/training/pi05_pt/train.sh
+
+# 关键 flags / key flags
+--policy.train_expert_only=true       # freeze PaliGemma, train only gemma_expert
+--policy.freeze_vision_encoder=true   # explicit redundant lock
+--policy.gradient_checkpointing=true  # 24GB VRAM under bf16
+--policy.dtype=bfloat16
+--policy.chunk_size=50
+--policy.n_action_steps=50
+--policy.max_state_dim=32
+--policy.max_action_dim=32
+--policy.optimizer_lr=2.5e-5
+--steps=10000  --save_freq=1000  --batch_size=1
+```
+
+Camera rename (LeIsaac 2-cam → π0.5 3-cam, missing `left_wrist` auto-padded inside modeling_pi05.py:1195):
+
+```python
+rename_map = {
+    "observation.images.front":  "observation.images.base_0_rgb",
+    "observation.images.wrist":  "observation.images.right_wrist_0_rgb",
+}
+```
+
+## 复现 / Reproduce
+
+```python
+from lerobot.policies.pi05 import PI05Policy
+policy = PI05Policy.from_pretrained("wsagi/Pi0.5-PickOrange")
+# 然后接 LeIsaac Isaac Sim eval pipeline
+# Then plug into the LeIsaac Isaac Sim eval pipeline:
+#   scripts/benchmark/run_one_strict.sh
+```
+
+20-round strict benchmark（distribution, 20 rounds × 3 episodes）：
+
+| P(placed=0) | P(placed=1) | P(placed=2) | P(placed=3) | E(🍊)/ep |
+|-------------|-------------|-------------|-------------|----------|
+| **95% (57/60)** | **5% (3/60)** | 0% | 0% | **0.05** |
+
+19/20 rounds 全 0/3，1 round 出现 1/3（Episode 8: placed=[F, T, F]）。Bernoulli noise distribution，无 task-completion signal。
+
+## 已 sweep 过的 ckpt / Checkpoints evaluated
+
+10k 训练每 1k 存一个，13 个 ckpt（500/1k/1.5k/.../10k）全 3-round 横评 = **1/60 oranges across 13 ckpts**，**全部 0/9 或 1/9**，无单调收敛迹象。ckpt-2000 是 3-round 抓到 2/9 的那个（最高），20-round 跑下来回归到 1/60，证实是 noise outlier 不是 signal。
+
+## 何时该用 / 不该用 / When (not) to use
+
+❌ **不要在生产环境使用** — 1.7% success rate 没有 task-completion 价值
+✅ **可以用作**：
+- π0.5 在低分辨率 VLM bottleneck 任务上的 baseline reference
+- "freeze VLM + train expert only" 配方失败案例的复现 ckpt
+- LeIsaac eval pipeline 的 π0.5 wire 协议验证 fixture
+
+## 替代方案 / Alternatives (better on same task)
+
+| Model | Strict | Where |
+|-------|--------|-------|
+| 🥇 GR00T-N1.7 (self-trained) | 68.3% | [`wsagi/GR00T-N1.6-PickOrange`](https://huggingface.co/wsagi) |
+| 🥈 SmolVLA (self-trained) | 25.0% | wsagi (待发布 / pending) |
+| 🥉 Diffusion Policy DDIM | 概率性 3/3 | [`wsagi/DiffusionPolicy-PickOrange`](https://huggingface.co/wsagi/DiffusionPolicy-PickOrange) |
+
+## License & Attribution
+
+- Apache-2.0
+- Base model: `lerobot/pi05_base` (Physical Intelligence × LeRobot)
+- Dataset: [`LightwheelAI/leisaac-pick-orange`](https://huggingface.co/datasets/LightwheelAI/leisaac-pick-orange)
+- Trained on RTX Pro 6000 96GB
+- Evaluated in Isaac Sim 5.1 + LeIsaac

config.json

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+{
+    "type": "pi05",
+    "n_obs_steps": 1,
+    "input_features": {
+        "observation.images.base_0_rgb": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                224,
+                224
+            ]
+        },
+        "observation.images.left_wrist_0_rgb": {
+            "type": "VISUAL",
+            "shape": [
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+                224,
+                224
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+            "shape": [
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+    "use_peft": false,
+    "push_to_hub": false,
+    "repo_id": null,
+    "private": null,
+    "tags": null,
+    "license": null,
+    "pretrained_path": "lerobot/pi05_base",
+    "paligemma_variant": "gemma_2b",
+    "action_expert_variant": "gemma_300m",
+    "dtype": "bfloat16",
+    "chunk_size": 50,
+    "n_action_steps": 50,
+    "max_state_dim": 32,
+    "max_action_dim": 32,
+    "num_inference_steps": 10,
+    "time_sampling_beta_alpha": 1.5,
+    "time_sampling_beta_beta": 1.0,
+    "time_sampling_scale": 0.999,
+    "time_sampling_offset": 0.001,
+    "min_period": 0.004,
+    "max_period": 4.0,
+    "use_relative_actions": false,
+    "relative_exclude_joints": [
+        "gripper"
+    ],
+    "action_feature_names": [
+        "shoulder_pan.pos",
+        "shoulder_lift.pos",
+        "elbow_flex.pos",
+        "wrist_flex.pos",
+        "wrist_roll.pos",
+        "gripper.pos"
+    ],
+    "rtc_config": null,
+    "image_resolution": [
+        224,
+        224
+    ],
+    "empty_cameras": 0,
+    "tokenizer_max_length": 200,
+    "normalization_mapping": {
+        "VISUAL": "IDENTITY",
+        "STATE": "QUANTILES",
+        "ACTION": "QUANTILES"
+    },
+    "gradient_checkpointing": true,
+    "compile_model": false,
+    "compile_mode": "max-autotune",
+    "freeze_vision_encoder": true,
+    "train_expert_only": true,
+    "optimizer_lr": 2.5e-05,
+    "optimizer_betas": [
+        0.9,
+        0.95
+    ],
+    "optimizer_eps": 1e-08,
+    "optimizer_weight_decay": 0.01,
+    "optimizer_grad_clip_norm": 1.0,
+    "scheduler_warmup_steps": 1000,
+    "scheduler_decay_steps": 30000,
+    "scheduler_decay_lr": 2.5e-06
+}

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train_config.json

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+{
+    "dataset": {
+        "repo_id": "LightwheelAI/leisaac-pick-orange",
+        "root": "/home/david/work/isaaclab-experience/LeIsaac/datasets/raw/leisaac-pick-orange",
+        "episodes": null,
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