Rewrite model card: full AWBC/LR-soup lineage, dataset & sibling links (team INHA-UNITED)

README.md

@@ -1,19 +1,46 @@
 ---
 license: apache-2.0
+library_name: openpi
+pipeline_tag: robotics
 tags:
 - robotics
 - vla
 - pi0.5
+- openpi
 - rby1
+- fold
+- awbc
+- model-soup
 - robocup
 ---
 
 # Doing Laundry — RoboCup 2026 Incheon
 
-**Team: INHA-UNITED**
+> **Team INHA-UNITED** · RB-Y1 T-shirt folding policy for the RoboCup 2026 @Home league (Incheon).
 
-RBY1 로봇의 빨래 개기(fold the t-shirt) 태스크용 Pi0.5 VLA 체크포인트 (Model Arithmetic greedy soup 병합본, `M0_lr_soup`).
+A π0.5 vision-language-action policy for Rainbow Robotics RB-Y1 T-shirt folding, deployed as the competition model. This is a **greedy model soup** (`M0_lr_soup`) — a weight average of a learning-rate sweep over the AWBC fold family, picked to maximize robustness on the real robot.
 
-- Base: `pi05_rby1_fold_v11_1prompt_f2r03_awbc`
-- Merge: LR sweep (lr1×2 + lr2 + lr4) greedy weight soup
-- Prompt: `"fold the t-shirt, Advantage: positive"`
+- **Method:** Model Arithmetic **greedy weight soup** of a 3-way LR sweep, all warm-started from the [v11 AWBC](https://huggingface.co/RB3159/pi05-rby1-fold-awbc) checkpoint (step 69999) and continued to step 99999. Members and soup weights: `lr1` (2.5e-6) ×2, `lr2` (5e-6) ×1, `lr4` (1e-5) ×1 — `lr1` was selected twice by greedy souping.
+- **Training objective:** Advantage-Weighted Behavior Cloning (AWBC) following χ₀ (kai0) — per-frame `awbc_weight` from the [advantage estimator](https://huggingface.co/RB3159/rby1-fold-advantage-estimator)'s relative advantage `V(s+H) − V(s)`, thresholded at a fixed global p70 cutoff (~30% positive). See [arXiv:2602.09021](https://arxiv.org/abs/2602.09021), [OpenDriveLab/kai0](https://github.com/OpenDriveLab/kai0).
+- **Prompt:** single task, `fold the t-shirt, Advantage: positive` (hold at inference).
+- **Architecture:** π0.5 (`pi05`, PaliGemma `gemma_2b` + action expert `gemma_300m`, action horizon 50).
+- **Lineage:** [pi05-rby1-fold-base](https://huggingface.co/RB3159/pi05-rby1-fold-base) (mir280) → [3stage](https://huggingface.co/RB3159/pi05-rby1-fold-3stage) → [v11 AWBC](https://huggingface.co/RB3159/pi05-rby1-fold-awbc) → LR sweep → **this soup**.
+- **Training data:** 432 episodes — [rby1-fold-v1.1](https://huggingface.co/datasets/RB3159/rby1-fold-v1.1) master + left/right mirror + weak time-scale augmentation + DAgger interventions, with a per-frame `awbc_weight` column, img224.
+- **Action space:** 16-dim — `[R_arm(7), R_gripper(1), L_arm(7), L_gripper(1)]`, delta on the 7-DoF arms, absolute on the grippers.
+- **Robot:** Rainbow Robotics RB-Y1 (dual-arm).
+
+## Why a soup
+
+The three LR-sweep runs share an init and a data distribution but converge to different basins. Averaging their weights (greedy soup) keeps the shared skill while smoothing over run-specific overfitting — no extra inference cost, and empirically the safest single model to deploy under competition conditions.
+
+## Files
+
+`params/` (orbax inference weights) + `assets/.../norm_stats.json` + `_CHECKPOINT_METADATA`. Trained/merged with [openpi](https://github.com/Physical-Intelligence/openpi).
+
+## Usage
+
+```bash
+python scripts/serve_policy.py --config pi05_rby1_fold_v11_1prompt_f2r03_awbc --checkpoint RB3159/Doing-Laundry-Robocup2026-Incheon
+```
+
+Hold the prompt to `fold the t-shirt, Advantage: positive`.