pravsels/pi05-bin-pack-reward-recap-mixed

pi0.5 Bin Pack — Reward Recap (Mixed)

Fine-tuned pi0.5 checkpoint for coffee capsule bin packing, trained with mixed positive/negative advantage conditioning (reward recap).

Experiment

• Objective: Test whether mixed positive/negative advantage conditioning improves bin-pack policy when fine-tuning from a task-trained checkpoint.
• Weight init: Resumed from pi05-bin-pack-single-dataset checkpoint (step 29999).
• Advantage mode: mixed — human demos are trained with prompt "pack coffee capsules into the cardboard bin container. Advantage: positive", policy-collected frames with "... Advantage: negative".
• Target steps: 100,000

Config

• Config name: pi05_bin_pack_coffee_capsules_reward_recap_mixed
• Model: pi0.5 (pi05=True, action_horizon=50)
• Batch size: 36
• Learning rate: 5e-5 cosine decay (10k warmup)
• Optimizer: AdamW (gradient clip norm 1.0)
• EMA decay: 0.999
• Delta actions: enabled

Dataset

9 LeRobot datasets (1 base + 8 dAgger rounds):

• villekuosmanen/bin_pick_pack_coffee_capsules
• villekuosmanen/dAgger_bin_pick_pack_coffee_capsules_1.0.0
• villekuosmanen/dAgger_bin_pick_pack_coffee_capsules_1.1.0
• villekuosmanen/dAgger_bin_pick_pack_coffee_capsules_1.2.0
• villekuosmanen/dAgger_bin_pick_pack_coffee_capsules_1.3.1
• villekuosmanen/dAgger_bin_pick_pack_coffee_capsules_1.4.0
• villekuosmanen/dAgger_bin_pick_pack_coffee_capsules_1.5.0
• villekuosmanen/dAgger_bin_pick_pack_coffee_capsules_1.5.1
• villekuosmanen/dAgger_bin_pick_pack_coffee_capsules_1.7.0

Loss Progression

Step	Loss
0	0.5005
25,000	0.0098
50,000	0.0075

Note: High initial loss (0.50) is expected — mixed mode introduces negative demonstrations the model hasn't seen. Loss dropped rapidly in the first few thousand steps.

Checkpoint Hashes

Verify integrity with tar cf - -C checkpoints/<step> params | sha256sum.

Step	Loss	SHA-256
25,000	0.0098	626c9cbce476d5e90abfefa57dda4322777240314630eb79abc5f37ff8f75ffb
50,000	0.0075	bec9d174f325623bc1c677e139001212c5a2c915810113be3872b45956fe609a
72,000	0.0061	2cdb1acbf6bdad15a681b219af63468d7c0af0f707764c433209f1d3b435a69c

Repo Structure

assets/                      # Norm stats for inference
checkpoints/<step>/params/   # Model weights (params only)
README.md                    # This file
TRAINING_LOG.md              # Training log

W&B

• Training dashboard

Usage

from openpi.training.config import get_config
from openpi.serving.policy_server import PolicyServer

config = get_config("pi05_bin_pack_coffee_capsules_reward_recap_mixed")
server = PolicyServer(config, checkpoint_path="checkpoints/<step>/params")