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README.md

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+---
+license: other
+task_categories:
+- robotics
+tags:
+- LeRobot
+- imitation-learning
+- vision-language-action
+- pi0.5
+- openpi
+- teleoperation
+language:
+- ko
+---
+
+# ETRI VR Robotics — FFW-SG2 텔레오퍼레이션 데이터 및 체크포인트
+
+ROBOTIS **AI Worker (FFW-SG2)** 를 Vision Pro VR로 텔레오퍼레이션해 수집한 모방학습
+데이터셋과, 이를 π0.5(openpi)로 파인튜닝한 체크포인트 저장소입니다.
+
+과제: **캔을 집어 접시에 놓기** (`put the can on the plate`)
+
+> 이 문서는 **데이터 추출 머신이 참조할 규약서**를 겸합니다. 새 데이터를 만들어 이
+> 저장소에 올리실 때 아래 "데이터 제출 규약"을 따라주세요.
+
+---
+
+## 저장소 구조
+
+```
+datasets/       LeRobot 형식 데이터셋 (학습 입력)
+checkpoints/    π0.5 LoRA 학습 결과 (.gitattributes 의 checkpoints/** 규칙으로 전부 LFS)
+info.json       LeRobot 스키마 참조용 템플릿
+```
+
+## 데이터셋 목록
+
+| 이름 | 에피소드 | 프레임 | fps | state | action | 비고 |
+|---|---|---|---|---|---|---|
+| `can1` | 6 | 6,903 | 10 | 19 | 16 | 초기 수집 (오른손) |
+| `can2` | 1 | 2,433 | 10 | 19 | 16 | 초기 수집 |
+| `can_left` | 19 | 17,346 | 10 | 19 | 16 | 왼손 수집 (28ep → 선별 19ep) |
+| `can_left_v2` | 29 | 23,096 | 10 | 19 | 16 | v2 스키마 |
+| **`can_left_v3`** | **29** | **19,944** | 10 | **25** | **17** | v3 스키마 |
+| **`can_left_v4`** | **9** | **5,463** | 10 | **25** | **17** | 0731 추가 수집 (v3와 동일 스키마) |
+| `raw_left_20260728` | — | — | — | — | — | 원본 에피소드 (LeRobot 변환 전) |
+| `raw_left_20260731` | — | — | — | — | — | 원본 에피소드 (tar, 9개) |
+
+카메라 4대 공통: `cam_head_l`, `cam_head_r`, `cam_wrist_l`, `cam_wrist_r` (640×480)
+
+`can_left_v3` 와 `can_left_v4` 는 스키마가 같아 합쳐 쓸 수 있습니다 (**38ep / 25,407프레임**).
+합칠 때는 `episode_index` 와 전역 `index` 를 이어붙이도록 재번호해야 합니다 — parquet 안에
+두 컬럼이 들어 있어 파일만 복사하면 인덱스가 충돌합니다.
+
+## v3 스키마 (현행)
+
+`convert_pkl_to_lerobot_v3.py` 로 생성. v2 대비 세 가지가 달라졌습니다.
+
+**state 25** = 기존 로봇 상태 19 + `can(x,y,z)` 3 + `plate(x,y,z)` 3
+
+객체 좌표 주석은 **비전이 아니라 실측 FK** 입니다. 캔은 파지점 기준(파지 전 고정 →
+파지 중 손에 부착 → 이후 고정), 접시는 놓는점 고정. 따라서 카메라 오차가 0입니다.
+
+**action 17** = 기존 16 + `lift` (몸 높이)
+
+리프트를 액션에 넣은 이유: 조작자가 "캔이 안 보이거나 책상에 가까우면 몸을 올리고,
+리치가 부족하면 몸을 낮추는" 전략을 쓰는데, v2까지는 이 전략이 데모에는 있으나
+정책이 출력할 수단이 없었습니다. 다음 프레임 측정값을 라벨로 하는 델타 액션입니다.
+
+**종료 트리밍** — 마지막 그리퍼 열림 + 2초까지만 사용합니다. VR 종료 버튼을 누르려고
+손을 드는 동작이 29ep 중 11ep를 오염시켜 3,152프레임(14%)을 제거했습니다.
+
+openpi 연결: `FFWSG2InputsV3` (오른손목 카메라는 마스크 + 제로 이미지 — 왼손 전용
+학습) / `LeRobotFFWSG2V3DataConfig` / config `pi05_can_left_v3_lora`
+
+---
+
+## 데이터 제출 규약
+
+새 데이터를 만들어 올리실 때 지켜주실 사항입니다.
+
+### 1. 형식
+
+LeRobot 형식(`data/`, `meta/`, `videos/`)으로 `datasets/<이름>/` 아래 두세요.
+`meta/info.json` 의 `total_episodes`, `total_frames` 가 실제 값과 일치해야 합니다.
+
+### 2. 학습/검증 분할 — 에피소드 단위 80/20
+
+```
+datasets/
+  <이름>_train/     학습용 80%
+  <이름>_val/       검증용 20%
+```
+
+**반드시 에피소드를 통째로 나눠주세요.** 프레임 단위로 섞어서 나누면 안 됩니다.
+10fps라 인접 프레임이 거의 동일한 이미지여서, 같은 에피소드가 양쪽에 걸치면
+검증 loss가 비현실적으로 낮게 나오고 아무 의미가 없어집니다.
+
+검증용 에피소드는 조명·캔 위치·촬영 시간대가 골고루 섞이게 골라주세요. 연속된
+구간을 그냥 잘라내면 특정 조건에 몰릴 수 있습니다.
+
+### 3. 큐레이션
+
+성공한 시연만 넣는 것이 기본 원칙이고 지금까지 그렇게 해왔습니다(28ep → 19ep 선별 등).
+
+다만 **실패-복구 데모는 포함하시길 권합니다.** 완전 실패가 아니라 *살짝 벗어났다가
+되돌아오는* 궤적입니다. 성공 궤적만 학습하면 정책은 성공 경로 위에서만 행동을 알고,
+한 번 벗어나면 그 상태가 학습 데이터에 없어 발산합니다(분포 이동). 실제로 v3 시연에서
+"접근 목표는 정확하나 파지 직전 데모에 없는 상태로 이탈"하는 실패가 반복됐습니다.
+
+### 4. 규모
+
+현재 38ep(v3 29 + v4 9)이 병목입니다. **100ep 이상**을 ��표로 하고 있습니다.
+
+### 5. 여러 데이터셋을 합칠 때
+
+parquet 안에 `episode_index` 와 전역 `index` 컬럼이 들어 있어, 파일을 복사만 하면
+인덱스가 충돌합니다. 이어붙일 때는 두 컬럼을 재번호해야 합니다.
+
+```
+v3 마지막  ep=28  index=19943
+v4 첫      ep=29  index=19944      ← 오프셋 = 앞 데이터셋의 total_frames
+```
+
+`meta/episodes.jsonl`, `meta/episodes_stats.jsonl` 의 `episode_index` 도 같이 옮기고,
+`meta/info.json` 의 `total_episodes` / `total_frames` / `total_videos` / `splits` 를
+갱신해야 합니다. 영상은 재인코딩 없이 파일명만 새 번호로 바꾸면 됩니다.
+
+---
+
+## 체크포인트
+
+| 경로 | 학습 데이터 | 입력 카메라 | 출력 액션 |
+|---|---|---|---|
+| `checkpoints/pi05_can1_lora/2999` | `can1` (6ep) | head_l + wrist_l | 16 (state 19) |
+| `checkpoints/pi05_can_left_v3_lora/2999` | `can_left_v3` (29ep) | head_l + wrist_l | 17 (state 25) |
+| `checkpoints/pi05_lift_head_lora/2999` | v3+v4 (38ep) | **head_l + head_r** | **1 — 리프트** |
+| `checkpoints/pi05_arm_wrist_lora/2999` | v3+v4 (38ep) | **wrist_l** | **16 — 팔+그리퍼** |
+
+각 약 8.9GB (params + train_state + assets). orbax OCDBT 형식이라 파일명에 확장자가
+없으며, `.gitattributes` 의 `checkpoints/** filter=lfs` 규칙으로 전부 LFS 추적됩니다.
+
+### 카메라×액션 분리 (2026-07-31)
+
+뒤의 두 체크포인트는 **한 쌍으로 함께 쓰는 모델**입니다. 각 카메라가 자기와 물리적으로
+함께 움직이는 축을 담당합니다.
+
+```
+헤드 스테레오(리프트에 장착)  →  리프트 1차원        action[16:17]
+왼손목      (팔 끝에 장착)    →  팔+그리퍼 16차원    action[0:16]
+```
+
+동기: 머리 카메라는 리프트에 붙어 있어 몸 높이가 바뀌면 픽셀→좌표 대응이 무효가 되고,
+손목 카메라는 팔과 함께 움직인다. 두 시점이 인지하는 거리가 서로 달라지는 구조라
+축을 나눠 학습했다.
+
+두 모델은 **데이터·스텝·학습률·배치가 전부 동일**하다 (38ep/25,407f, 3,000 step,
+batch 16, peak_lr 5e-5). 입력 카메라와 담당 액션만 다르므로 결과 차이를 그 두 가지로
+귀속할 수 있다.
+
+추론 시에는 두 모델을 각각 돌려 17차원으로 조립한다 —
+`pi05_inference_bridge.py --split-models` (SplitPolicy). 조립 결과가 단일 17차원 모델과
+같은 규약이라 하류(청크 실행·FK·relay·안전 계층)는 수정이 필요 없다.
+
+| | 리프트(헤드) | 팔(손목) |
+|---|---|---|
+| 최종 loss 중앙값 | 0.00030 | 0.03740 |
+| MAE (학습셋 150프레임) | 0.02181 | 0.05547 |
+| "항상 0" 기준선 대비 | +86.6% | +87.8% |
+| 예측/실제 표준편차 비 | 1.13 | 0.89~1.02 |
+| 예측·실제 상관계수 | 0.950 | 0.875~0.991 |
+
+**두 모델의 loss 를 서로 비교하지 말 것** — 1차원이 16차원보다 쉬워서 낮게 나온다.
+자명한 기준선("항상 0", "항상 평균") 대비 얼마나 이겼는지로 보는 것이 맞다. 위 표는
+두 모델 모두 상수를 출력하는 것이 아니라 실제 변화를 따라간다는 것을 보여준다.
+
+단, 이 수치는 **학습에 쓴 프레임으로 잰 것**이라 일반화를 보장하지 않는다. 그리고
+액션 예측 정확도와 파지 성공률은 별개다 (아래 "알려진 제약" 참조).
+
+### 학습 기준선 (`pi05_can_left_v3_lora/2999`)
+
+```
+모델      π0.5 LoRA (paligemma gemma_2b_lora + action expert gemma_300m_lora), EMA off
+데이터    can_left_v3 — 29ep / 19,944 frames  (batch 16 기준 2.41 epoch)
+스텝      3,000  (batch 16, action_horizon 10)
+학습률    cosine — warmup 100 → peak 5e-5 → decay 3,000 → 5e-6, AdamW clip 1.0
+하드웨어  RTX 6000 Ada 48GB × 1 (공유 환경, 실효 가용 약 39GB)
+소요      2시간 34분  (3.0~3.1 s/step)
+```
+
+loss 중앙값 추이 (300스텝 구간):
+
+| 구간 | 0~299 | 600~899 | 1200~1499 | 1800~2099 | 2400~2699 | 2700~2999 |
+|---|---|---|---|---|---|---|
+| loss | 0.0912 | 0.0609 | 0.0479 | 0.0398 | 0.0319 | **0.0314** |
+
+역전 없이 단조 감소했고 종료 시점에도 하락 추세가 남아 있었습니다(스케줄 종료로 중단).
+발산 지표: `loss>0.3` 10건/3,000, `grad_norm>1.0`(클리핑) 19건/3,000.
+
+> **loss가 낮은 것과 캔을 집는 것은 별개입니다.** 이 값은 액션 예측 정확도이지 작업
+> 성공률이 아닙니다. 성공률은 실물 롤아웃으로만 측정되며, 100회 이상 시행해야 유의미한
+> 수치가 나옵니다. 이 체크포인트는 **기준선**으로 쓰기 위한 것입니다.
+
+---
+
+## 사용법
+
+```bash
+git clone https://huggingface.co/datasets/ETRI7203/ETRI_VR_Robotics_data_research hf_data
+# git-lfs 필수. 없으면 17GB가 포인터 텍스트로만 내려옵니다.
+
+mkdir -p ~/.cache/huggingface/lerobot/etri
+ln -s $(pwd)/hf_data/datasets/can_left_v3 ~/.cache/huggingface/lerobot/etri/can_left_v3
+```
+
+openpi 학습 코드와 설정은 ���도 저장소(`ETRI_VR_Robotics_data_research`, 브랜치
+`verify/assist-phase2`)의 `pipeline/vla/ffw_sg2/openpi_port/` 에 있습니다.
+
+## 알려진 제약
+
+- **그리퍼 실구동 경로 미구현** — 현재 Cyclo 경로에 그리퍼 컨트롤러가 없어, 정책이
+  파지 동작을 출력해도 물리적으로 쥐지 못합니다. 파지 성공률 측정의 선결 조건입니다.
+- 성공률 미달성 — 접근까지는 정확하나 파지 직전 이탈이 반복됩니다. 병목은 데이터 규모로
+  판정됐습니다(시스템 결함 아님).
+- `raw_left_20260728` 은 LeRobot 변환 전 원본이라 그대로 학습에 쓸 수 없습니다.

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