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.

Method: Model Arithmetic greedy weight soup of a 3-way LR sweep, all warm-started from the v11 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's relative advantage V(s+H) − V(s), thresholded at a fixed global p70 cutoff (~30% positive). See arXiv:2602.09021, 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 (mir280) → 3stage → v11 AWBC → LR sweep → this soup.
Training data: 432 episodes — 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.

Usage

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.