== 1. tar を広げる → /workspace/data/mecharm_redblock_box_deep_even10_v1
   エピソード 50 本 (Mac で作ったときは 50 本)
== 2. 学習設定 pi05_mecharm_deep_even10_lora を足す (steps=3000. π0.5 base から)
installed: pi05_mecharm_deep_even10_lora (repo_id=local/mecharm_redblock_box_deep_even10_v1, 3000 steps, init=π0.5 base)
== 3. LeRobot 形式に変換 (local/mecharm_redblock_box_deep_even10_v1)

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  run_0001_20261109_131103: 146 steps
done: 50 episodes / 7128 frames → /root/.cache/huggingface/lerobot/local/mecharm_redblock_box_deep_even10_v1
== 4. 正規化の値をこの 50 本で測り直す (compute_norm_stats.py --config-name pi05_mecharm_deep_even10_lora)

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Writing stats to: /workspace/openpi/assets/pi05_mecharm_deep_even10_lora/local/mecharm_redblock_box_deep_even10_v1
   /workspace/openpi/assets/pi05_mecharm_deep_even10_lora/local/mecharm_redblock_box_deep_even10_v1/norm_stats.json (sha256 813fcb8cccd1…)
NORM sha256=813fcb8cccd147c1d49e5c06e695587142d4535ff7c1f27b9fc94ea6aee66ce4 path=assets/pi05_mecharm_deep_even10_lora/local/mecharm_redblock_box_deep_even10_v1/norm_stats.json
== 5. 準備できました. 学習はこのコマンドで始める (このスクリプトでは流さない):
   cd /workspace/openpi && XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 uv run scripts/train.py pi05_mecharm_deep_even10_lora --exp-name=deep_even10_v1 --overwrite 2>&1 | tee /workspace/logs/train_deep_even10_v1.log