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RB3159/Doing-Laundry-Robocup2026-Incheon
Doing-Laundry-Robocup2026-Incheon is a robotics model from RB3159. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for openpi. The card lists the license as apache-2.0.
Team INHA-UNITED · RB-Y1 T-shirt folding policy for the RoboCup 2026 @Home league (Incheon).
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Updated Jul 4, 2026
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From the Hugging Face model README
Team INHA-UNITED · RB-Y1 T-shirt folding policy for the RoboCup 2026 @Home league (Incheon).
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.
lr1 (2.5e-6) ×2, lr2 (5e-6) ×1, lr4 (1e-5) ×1 — lr1 was selected twice by greedy souping.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.fold the t-shirt, Advantage: positive (hold at inference).pi05, PaliGemma gemma_2b + action expert gemma_300m, action horizon 50).awbc_weight column, img224.[R_arm(7), R_gripper(1), L_arm(7), L_gripper(1)], delta on the 7-DoF arms, absolute on the grippers.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.
params/ (orbax inference weights) + assets/.../norm_stats.json + _CHECKPOINT_METADATA. Trained/merged with openpi.
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.