classify_makovian.md

Markovian Task Classification — Run Commands

Chạy từ thư mục gốc: cd /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics

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Bước 1 — Khởi động vLLM server (Terminal riêng, giữ chạy nền)

bash scripts/launch_qwen3vl_server.sh

Chờ đến khi thấy: Application startup complete (~2–5 phút lần đầu tải model).

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Bước 2 — Phân loại DROID (20 chunks)

python scripts/classify_markovian_qwen.py

Output:

• scripts/task_labels_cache.jsonl — cache resumable
• scripts/task_labels.csv — ~49k task labels

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Bước 3 — Join labels về episodes + sample 130/label (DROID)

python scripts/join_labels_to_episodes.py

Output:

• scripts/episode_markovian_split_full.csv — toàn bộ episodes trong 20 chunks
• scripts/episode_markovian_split.csv — 130 markovian + 130 non_markovian

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Bước 4 — Phân loại GR1 humanoid (DreamDojo-HV + EgoDex)

python scripts/classify_markovian_gr1.py

Output:

• scripts/gr1_task_labels_cache.jsonl — cache resumable
• scripts/gr1_task_labels.csv — toàn bộ 133 episodes
• scripts/gr1_episode_sampled.csv — ≤33/label/source (cân bằng 2 nguồn)

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Tóm tắt output files

File	Mô tả
scripts/task_labels.csv	DROID: 49k task strings + label
scripts/episode_markovian_split_full.csv	DROID: tất cả episodes trong 20 chunks
scripts/episode_markovian_split.csv	DROID: 260 episodes sampled (130×2)
scripts/gr1_task_labels.csv	GR1: 133 episodes + label
scripts/gr1_episode_sampled.csv	GR1: ~132 episodes sampled (33×2×2)