Dexverse-FunctionalHammerStrike-v0 floating_shadow_right: DP image, best step 044400, last step 066600, 25/50 (vast-isaac, 20261101-133446_vast-isaac_hammerstrike-shadow)

v0/teleop_lerobot/functional/Dexverse-FunctionalHammerStrike-v0/floating_shadow_right/README.md

@@ -0,0 +1,80 @@
+# Dexverse-FunctionalHammerStrike-v0 / floating_shadow_right — Diffusion Policy (image, 240×320)
+
+LeRobot 0.4.2 official Diffusion Policy trained on the teleop demos `Dexverse-CSI/dv-demos-v0` → `v0/teleop_lerobot/functional/Dexverse-FunctionalHammerStrike-v0/floating_shadow_right` (50 episodes).
+
+| 항목 | 값 |
+|---|---|
+| run_id | `20261101-133446_vast-isaac_hammerstrike-shadow` |
+| 학습 서버 | vast-isaac |
+| 학습 기간 (KST) | 2026-11-02T21:55:35+09:00 → 2026-11-03T09:16:47+09:00 |
+| 코드 | https://github.com/ycyao216/DexVerse.git @ 8d0a6b5d7ccbcafe3a0b31852721a4adb9f8a50e (clean), 학습·평가 스크립트 `experiments/vast-isaac/20261031_teleop_single/teleop_dp_image/` |
+| 명령 | `RID=20261101-133446 NUM_WORKERS=24 run_sets.sh 1 FunctionalHammerStrike:shadow_right` |
+| seed | 42 |
+| best 체크포인트 | `best/` = 학습 step 044400 (50 epoch마다 저장한 스냅숏 중 pkl 에피소드 0–9 성공 수가 가장 큰 것, 동률이면 뒤 스냅숏) |
+| last 체크포인트 | `last/` = 최종 step 066600 (300 epoch 끝, 평가 안 함, 재개용) |
+| **최종 평가** | **25/50 (0.50)** — pkl 에피소드 0–49의 initial_state 복원, DDPM 100 step, 8 step 실행, 최대 3000 step, 시드 없음 |
+
+## 스냅숏 선택 (에피소드 0–9, 10회)
+
+| step | 성공 |
+|---|---|
+| 011100 | 0/10 |
+| 022200 | 1/10 |
+| 033300 | 3/10 |
+| 044400 | 4/10 |
+| 055500 | 3/10 |
+
+## config.yaml
+
+```yaml
+task: Dexverse-FunctionalHammerStrike-v0
+hand: shadow_right
+input_width: 320
+pickle: /workspace/datasets/dexverse/teleop_pkl_bs1/functional/Dexverse-FunctionalHammerStrike-v0/Dexverse-FunctionalHammerStrike-v0_20261030_061058_floating_shadow_right.pkl
+dataset_root: /workspace/datasets/dexverse/teleop_lerobot/Dexverse-FunctionalHammerStrike-v0/shadow_right/functionalhammerstrike-v0-floating_shadow_right
+checkpoints: /workspace/checkpoints/20261031_teleop_single/20261101-133446_vast-isaac_hammerstrike-shadow
+epochs: 300
+batch_size: 64
+seed: 42
+num_workers: 24
+policy: lerobot diffusion
+separate_rgb_encoders: true
+resize_stage: dataloader
+```
+
+## settings.md (실험 폴더 자동 생성)
+
+# 20261101-133446_vast-isaac_hammerstrike-shadow
+
+## 태스크·데이터
+- 태스크 Dexverse-FunctionalHammerStrike-v0, 손 shadow_right
+- 데모 pkl: /workspace/datasets/dexverse/teleop_pkl_bs1/functional/Dexverse-FunctionalHammerStrike-v0/Dexverse-FunctionalHammerStrike-v0_20261030_061058_floating_shadow_right.pkl
+- 데이터셋: /workspace/datasets/dexverse/teleop_lerobot/Dexverse-FunctionalHammerStrike-v0/shadow_right/functionalhammerstrike-v0-floating_shadow_right (LeRobot v3.0, 50 에피소드 14181 프레임, 60 fps, 전체 학습, val 분할 없음)
+
+## 입력 (Input)
+- 이미지: 사용, ['observation.images.third_person', 'observation.images.eye_in_hand'], 480×640 → 240×320 bilinear antialias resize (dataloader 단계), crop [216, 288] (학습 랜덤, 평가 중앙)
+- 상태: observation.state 28차원 (joint_pos), 관측 길이 2
+- 정규화: {'VISUAL': 'MEAN_STD', 'STATE': 'MIN_MAX', 'ACTION': 'MIN_MAX'}
+
+## 출력 (Output)
+- action 28차원 (env 행동 그대로), 예측 16, 실행 8, 60 Hz
+
+## 모델
+- LeRobot 0.4.2 Diffusion Policy, 카메라별 ResNet18, UNet [512, 1024, 2048]
+
+## 학습 하이퍼파라미터
+- Adam lr 0.0001, cosine warmup 500, batch 64, 300 epoch, grad clip 10, EMA 없음, seed 42, fp32, early stop 없음, 데이터 worker 24
+- 학습 코드 commit 8d0a6b5d7ccbcafe3a0b31852721a4adb9f8a50e
+
+## 추론(평가) 하이퍼파라미터
+- 체크포인트: best = 044400 4/10 (평가), last = 최종 step (재개용 보관, 평가 안 함)
+- DDPM 100 step, 8 step 실행, 최대 3000 step
+- pkl 에피소드 50개의 initial_state 복원, 시드 없음, 평가 코드 commit 8d0a6b5d7ccbcafe3a0b31852721a4adb9f8a50e
+- 결과: best 25/50
+
+## 사용
+
+```python
+from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
+policy = DiffusionPolicy.from_pretrained("<local path to best/>")   # lerobot==0.4.2; 입력 이미지는 240×320으로 resize해서 넣는다
+```

v0/teleop_lerobot/functional/Dexverse-FunctionalHammerStrike-v0/floating_shadow_right/best/config.json

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+{
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+    "noise_scheduler_type": "DDPM",
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+    "beta_schedule": "squaredcos_cap_v2",
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+    "optimizer_weight_decay": 1e-06,
+    "scheduler_name": "cosine",
+    "scheduler_warmup_steps": 500
+}

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v0/teleop_lerobot/functional/Dexverse-FunctionalHammerStrike-v0/floating_shadow_right/meta.json

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v0/teleop_lerobot/functional/Dexverse-FunctionalHammerStrike-v0/floating_shadow_right/select.log

@@ -0,0 +1,5 @@
+011100 0/10
+022200 1/10
+033300 3/10
+044400 4/10
+055500 3/10

v0/teleop_lerobot/functional/Dexverse-FunctionalHammerStrike-v0/floating_shadow_right/settings.md

@@ -0,0 +1,27 @@
+# 20261101-133446_vast-isaac_hammerstrike-shadow
+
+## 태스크·데이터
+- 태스크 Dexverse-FunctionalHammerStrike-v0, 손 shadow_right
+- 데모 pkl: /workspace/datasets/dexverse/teleop_pkl_bs1/functional/Dexverse-FunctionalHammerStrike-v0/Dexverse-FunctionalHammerStrike-v0_20261030_061058_floating_shadow_right.pkl
+- 데이터셋: /workspace/datasets/dexverse/teleop_lerobot/Dexverse-FunctionalHammerStrike-v0/shadow_right/functionalhammerstrike-v0-floating_shadow_right (LeRobot v3.0, 50 에피소드 14181 프레임, 60 fps, 전체 학습, val 분할 없음)
+
+## 입력 (Input)
+- 이미지: 사용, ['observation.images.third_person', 'observation.images.eye_in_hand'], 480×640 → 240×320 bilinear antialias resize (dataloader 단계), crop [216, 288] (학습 랜덤, 평가 중앙)
+- 상태: observation.state 28차원 (joint_pos), 관측 길이 2
+- 정규화: {'VISUAL': 'MEAN_STD', 'STATE': 'MIN_MAX', 'ACTION': 'MIN_MAX'}
+
+## 출력 (Output)
+- action 28차원 (env 행동 그대로), 예측 16, 실행 8, 60 Hz
+
+## 모델
+- LeRobot 0.4.2 Diffusion Policy, 카메라별 ResNet18, UNet [512, 1024, 2048]
+
+## 학습 하이퍼파라미터
+- Adam lr 0.0001, cosine warmup 500, batch 64, 300 epoch, grad clip 10, EMA 없음, seed 42, fp32, early stop 없음, 데이터 worker 24
+- 학습 코드 commit 8d0a6b5d7ccbcafe3a0b31852721a4adb9f8a50e
+
+## 추론(평가) 하이퍼파라미터
+- 체크포인트: best = 044400 4/10 (평가), last = 최종 step (재개용 보관, 평가 안 함)
+- DDPM 100 step, 8 step 실행, 최대 3000 step
+- pkl 에피소드 50개의 initial_state 복원, 시드 없음, 평가 코드 commit 8d0a6b5d7ccbcafe3a0b31852721a4adb9f8a50e
+- 결과: best 25/50