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README.md

@@ -30,42 +30,36 @@ _An [X-VLA](https://arxiv.org/abs/2510.10274) (Florence2 + Soft-Prompted Transfo
 - [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）
 
-**📌 Branches**: `main` = 17k (current best, 50% 6-round per-ep) · `ckpt-10k` (4/9 bench, 33% 6-round) · `ckpt-15k` (历史, 22% bench)
-
 ## 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 / **17k step**（10k from base + 5k resume + 2k resume）/ **weak image-aug (brightness ±5% only)** / GRIPPER_SCALE=5 / ~30 min on RTX 4090。
-- **评测 / Eval**：
-  - **6-round (18 ep × 60s)**: **9/18 (50%)**，**6/6 ep 全 placed (1,2,2,2,1,2)** — 这是 session 中 consistency 最佳的 ckpt。
-  - **Benchmark-aligned 3-round (× 120s × 180s wall)**: 4/9 (44%) — 与 10k/15k 持平（3-round 方差大无法区分）。
+- **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_
 
-- **6-round consistency 完美**: 18 ep 中 **18/18 episodes 至少抓起一个橙子** (1,2,2,2,1,2 per ep)。其他 baseline / earlier ckpts 通常 2-3 个 ep 为 0/3。
-  _Perfect 6-round consistency: every single one of 18 episodes placed at least 1 orange. Other baselines (10k, ACT, DP, X-VLA-15k) had 2-3 zero-orange episodes._
+- **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 + extended training**：90+ 实验中只有 weak image-aug (brightness ±5%) retrain 是聚合正向 (+5.6% vs baseline)；training step 7k → 17k 持续刷新 peak。
-  _Weak image-aug (brightness ±5% only, max_num_transforms=1) was the only aggregate-positive retrain in 90+ experiments. Extending training from 7k to 17k progressively raised the 6-round peak (33% → 50%)._
+- **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
-# 第一段 10k step from lerobot/xvla-base
-WEAK_IMAGE_AUG=1 BATCH_SIZE=8 MAX_STEPS=10000 SAVE_FREQ=500 \
-OUTPUT_DIR=$LEISAAC/outputs/xvla-leisaac-pick-orange.weakaug \
-bash LeIsaac/scripts/finetune/xvla/train.sh
-
-# 续训 → 17k (15k 时也 save 了一份，但 17k 是 best peak)
-WEAK_IMAGE_AUG=1 BATCH_SIZE=8 MAX_STEPS=17000 SAVE_FREQ=500 RESUME=true \
+# 一段式 10k step from lerobot/xvla-base
+WEAK_IMAGE_AUG=1 \
+BATCH_SIZE=8 \
+MAX_STEPS=10000 \
+SAVE_FREQ=500 \
 OUTPUT_DIR=$LEISAAC/outputs/xvla-leisaac-pick-orange.weakaug \
 bash LeIsaac/scripts/finetune/xvla/train.sh
 ```
@@ -78,6 +72,10 @@ bash LeIsaac/scripts/finetune/xvla/train.sh
 --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）
@@ -94,10 +92,10 @@ bash server/serve_xvla.sh --detach
 POLICY_PORT=5558 \
 POLICY_TIMEOUT_MS=3000 \
 ACTION_HORIZON=1 \
-EVAL_ROUNDS=6 \
-EPISODE_LENGTH=60 \
+EVAL_ROUNDS=3 \
+EPISODE_LENGTH=120 \
 PROMPT="Pick up the orange and put it in the plate" \
-MAX_ROUND_WALL_S=90 \
+MAX_ROUND_WALL_S=180 \
 bash server/eval_pi05.sh
 ```
 
@@ -109,48 +107,37 @@ bash server/eval_pi05.sh
 |---|---|---|---|
 | 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)** | **9/18 + 6/6 consistency** ⭐ | **50%** | 全 chunk 复用，单 chunk 自洽 |
+| **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_
 
-### 6-round eval (18 ep × 60s × 90s wall_cap)
+### Benchmark-aligned (3 round × 120s sim × 180s wall_cap) — leaderboard 同条件
 
-| Episode | oranges placed | wall time |
-|---|---|---|
-| 1 | 2/3 | 90.0s |
-| 2 | 1/3 | 90.0s |
-| 3 | 2/3 | 90.0s |
-| 4 | 1/3 | 90.0s |
-| 5 | 2/3 | 90.1s |
-| 6 | 1/3 | 90.0s |
-| **Total** | **9/18 (50%)** | — | **6/6 ep ≥1 orange ⭐** |
-
-### Benchmark-aligned 3-round (120s × 180s wall, leaderboard 同条件)
-
-| Episode | oranges placed |
-|---|---|
-| 1 | 2/3 |
-| 2 | 1/3 |
-| 3 | 1/3 |
-| **Total** | **4/9 (44%)** |
-
-注：3-round 方差大，10k/15k/17k 在 benchmark 上都 ≈ 4/9，但 6-round (18 ep) 视角差异显著 (10k 33% < 15k 22% < 17k 50%)。
-
-### Weak aug 完整 ckpt 曲线 (6-round @ h=32)
+| 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）|
 
-| step | 6k | 7k | 8k | 9k | 10k | 11k | 12k | 13k | 14k | 15k | 16k | **17k** | 18k | 19k | 20k |
-|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
-| oranges | 6 | 5 | 6 | 4 | 6 | 4 | 5 | 4 | 4 | 7 | 5 | **9** | 5 | 7 | 5 |
-| per-ep% | 33 | 28 | 33 | 22 | 33 | 22 | 28 | 22 | 22 | 39 | 28 | **50** | 28 | 39 | 28 |
+### 6-round 扩展 eval (60s sim × 90s wall_cap)
 
-**Pattern**: peak 每 ~5-7k step 出现 (10k, 15k, 17k)，17k 为当前 best。19k = 39% 也是高位但不及 17k。20k 后趋势未明确，需续训验证 overfit 边界。
+| 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, 早期 6-10k 范围) | per-ep aggregate | vs baseline |
+| 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 |
@@ -159,14 +146,14 @@ _Evaluation_
 | 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)。
+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：
+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。RF + 10-step denoising 本身就有平滑性。
+- ❌ **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。
@@ -178,10 +165,10 @@ _Negative findings — DO NOT repeat_
 
 ## 限制 / Limitations
 
-- **样本数**：50% per-ep 是 6-round (18 ep) 估计，CI ±15%。Benchmark 3-round (9 ep) CI 更宽 ±20%。
-- **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 ~50% per-ep 上限。
-- **place 子任务多模态**：模型偶尔抓起后放偏位（仍 placed 1-2/3，但未 3/3 perfect）。可能需要 DAgger 或 synthetic relabel。
-- **Overfit 边界**：本 ckpt 在 17k step（继续训到 25k 验证）。历史上 40k 是深度 overfit ("连碰都不准")。
+- **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
 

config.json

@@ -57,7 +57,7 @@
     "private": null,
     "tags": null,
     "license": null,
-    "pretrained_path": "/home/david/work/isaaclab-experience/LeIsaac/outputs/xvla-leisaac-pick-orange.weakaug/checkpoints/last/pretrained_model",
+    "pretrained_path": "lerobot/xvla-base",
     "chunk_size": 32,
     "n_action_steps": 8,
     "dtype": "bfloat16",
@@ -208,7 +208,7 @@
         224,
         224
     ],
-    "num_image_views": 5,
+    "num_image_views": 4,
     "empty_cameras": 1,
     "freeze_vision_encoder": true,
     "freeze_language_encoder": true,

model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:96f9993b6603cda5c13b4f58727e6ee59cb4b1bd33c49b257b7e95930bbd814a
+oid sha256:a2142145a9c4fe618dd35ceaadba6b7e6be3391f2b09379d2d553efe951d718d
 size 1759596986

train_config.json

@@ -137,7 +137,7 @@
         "private": null,
         "tags": null,
         "license": null,
-        "pretrained_path": "/home/david/work/isaaclab-experience/LeIsaac/outputs/xvla-leisaac-pick-orange.weakaug/checkpoints/last/pretrained_model",
+        "pretrained_path": "lerobot/xvla-base",
         "chunk_size": 32,
         "n_action_steps": 8,
         "dtype": "bfloat16",
@@ -288,7 +288,7 @@
             224,
             224
         ],
-        "num_image_views": 5,
+        "num_image_views": 4,
         "empty_cameras": 1,
         "freeze_vision_encoder": true,
         "freeze_language_encoder": true,
@@ -311,14 +311,14 @@
     "reward_model": null,
     "output_dir": "/home/david/work/isaaclab-experience/LeIsaac/outputs/xvla-leisaac-pick-orange.weakaug",
     "job_name": "xvla",
-    "resume": true,
+    "resume": false,
     "seed": 1000,
     "cudnn_deterministic": false,
     "num_workers": 4,
     "batch_size": 8,
     "prefetch_factor": 4,
     "persistent_workers": true,
-    "steps": 20000,
+    "steps": 10000,
     "eval_freq": 20000,
     "log_freq": 200,
     "tolerance_s": 0.0001,
@@ -366,5 +366,5 @@
         "observation.images.front": "observation.images.image",
         "observation.images.wrist": "observation.images.image2"
     },
-    "checkpoint_path": "/home/david/work/isaaclab-experience/LeIsaac/outputs/xvla-leisaac-pick-orange.weakaug/checkpoints/last"
+    "checkpoint_path": null
 }