Datasets:

RobotisAI
/

evButtonPush-260423-01-hdf5

---
license: apache-2.0
task_categories:
  - robotics
tags:
  - robotics
  - manipulation
  - imitation-learning
  - elevator-button-press
  - isaac-sim
  - isaac-lab
  - omy
pretty_name: ev Button Push v02 (OMY call-button, contact-force teacher)
size_categories:
  - 10B<n<100B
---

# evButtonPush — 260423-01 (v02)

## 한국어

**ROBOTIS OMY** 6-DoF 팔이 **NVIDIA Isaac Sim / Isaac Lab** 환경에서 엘리베이터 호출버튼을 누르는 시뮬레이션 데모셋 (v02). 스크립트된 IK teacher로 자동 수집됨.

> 이전 collection (거리 기반 v01) 은
> [`RobotisAI/evButtonPush-260422-01-hdf5`](https://huggingface.co/datasets/RobotisAI/evButtonPush-260422-01-hdf5)
> 에서 받을 수 있습니다 (baseline / ablation 용).

### v02 특성 (권장)
- **45 demos**, seed `0`~`44`, 전부 pressed=True
- 약 35초/ep, ~1050 프레임 @ 30fps
- 성공 판정: **물리 접촉 힘 ≥ 0.5 N** (PhysX `tip_contact` sensor)
- 엘베 **문 FSM**: `state 0 (대기) → 1 (눌림, LED on) → 2 (문 여는 중) → 3 (완전 개방)`
- 평균 접촉 힘 피크: 60~80 N
- Press 목표 Z offset: `button_base` 기준 **+0.11 m** 고정
- `call_button_lit` LED 관찰 포함
- Teacher CLI:
  ```
  --approach-steps 100   --press-steps 400
  --stall-steps 20       --hold-steps 30
  --return-steps 300     --home-idle-steps 150
  --press-depth 0.035    --contact-force-threshold 0.5
  ```

### Task
- Task ID (Isaac Lab): `RobotisLab-CallButton-Right-OMY-v0`
- 로봇: ROBOTIS **OMY** (6축 팔 + 2지 그리퍼)
- Pedestal 높이: 0.908 m, 그리퍼 항상 닫힘 (stiffness 2×10⁶, effort 5000)
- 엘리베이터 USD: 100종 variants 중 seed별 random_choice (`elevator_setup_new`)
- 로봇 초기 자세 랜덤 (seed별):
  - 버튼까지 거리(standoff): 0.55 ~ 0.65 m
  - 좌우 lateral: −0.05 ~ 0.05 m
  - yaw: 0°

### 카메라

3대 모두 **RGB, 1920 × 1200, Pinhole**:
- 수평 aperture 20.955 mm
- 수직 aperture 13.097 mm (16:10 비율로 자동 계산)
- clipping (0.01, 100) m

| 이름 | prim_path | 기준 Frame | 위치 (m) | Quat (w, x, y, z) | FOV (H / V / D) | focal |
|---|---|---|---|---|---|---|
| `cam_wrist` | `Robot/OMY/link6/cam_wrist` | link6 (팔과 함께 이동) | (0.000, −0.080, 0.070) | (0.0018, −0.0018, 0.7071, 0.7071) | 83.21° / 58.06° / 92.64° | 11.8 mm |
| `cam_top` | `Robot/OMY/world/cam_top` | 로봇 base | (0.000233, 0.1499, 0.43953) | (0.5144, 0.4146, −0.5064, −0.5542) | 92.67° / 66.44° / 102.03° | 10.0 mm |
| `cam_belly` | `Robot/OMY/world/cam_belly` | 로봇 base | (0.08705, 0.1499, 0.1075) | (−0.4901, −0.5097, 0.5097, 0.4901) | 92.67° / 66.44° / 102.03° | 10.0 mm |

OMY USD 내부의 `world` prim은 시뮬레이션 월드가 아니라 **로봇 base (link0)**를 가리킴.

```
world_pose(cam_top)   = robot.root_pose_w ⊙ offset_cam_top
world_pose(cam_wrist) = robot.root_pose_w ⊙ FK(link0→link6) ⊙ offset_cam_wrist
```

### HDF5 내부 구조 (episode 당)

```
data/demo_0/
├── actions              (T, 7)    float32   arm 6 + gripper 1
├── processed_actions    (T, 10)   float32   전체 joint target
├── obs/
│   ├── cam_wrist        (T, 1200, 1920, 3)   uint8
│   ├── cam_top          (T, 1200, 1920, 3)   uint8
│   ├── cam_belly        (T, 1200, 1920, 3)   uint8
│   ├── joint_pos                (T, 10)     float32
│   ├── joint_vel                (T, 10)     float32
│   ├── call_button_pos          (T, 3)      float32   버튼 world 좌표
│   ├── rel_ee_call_button_distance (T, 3)   float32   tip-button 벡터
│   └── call_button_lit          (T, 1)      bool      ⭐ LED on/off
├── states/articulation/robot/...
├── states/rigid_object/pedestal/...
└── initial_state/...
```

### 파일 구성
- `demo_XX.hdf5` - episode 하나당 파일 하나
- `demo_XX.log` - teacher stdout (`[SETUP]`, `[PHASE]`, `[PRESSED]`, `[RESULT]` 마커)
- `summary.log` - demo당 한 줄 요약

### 추천 사용 방법
Imitation learning (ACT, Diffusion Policy, BC) 학습 시 `qpos_dim = 8`:
```python
qpos   = concat(joint_pos[:6], gripper_closed_flag, call_button_lit)  # 8
action = [arm_delta[:6], gripper_cmd]                                 # 7
images = {cam_wrist, cam_top, cam_belly}                              # 1920×1200 (필요시 resize)
```

일반적으로 학습 파이프라인에서는 **240×320으로 resize**해서 씁니다. 원본은 1920×1200으로 보존.

---

## English

Simulated demonstrations of a **ROBOTIS OMY** 6-DoF arm pressing an elevator
call-button, collected in **NVIDIA Isaac Sim / Isaac Lab** with a scripted
IK teacher. This dataset is the **v02** collection — improved over v01 with
physics-based success detection and elevator-door FSM.

> The earlier v01 collection (distance-based teacher, baseline only) is at
> [`RobotisAI/evButtonPush-260422-01-hdf5`](https://huggingface.co/datasets/RobotisAI/evButtonPush-260422-01-hdf5).

### v02 highlights (recommended for training)

- **45 demos**, seeds `0` – `44`, all pressed = True
- ~35 s per episode, ~1050 frames at 30 fps
- Success criterion: **physical contact force ≥ 0.5 N** (PhysX `tip_contact` sensor)
- Elevator **door FSM** progresses on press:
  `state 0 (idle) → 1 (pressed / LED on) → 2 (door opening, progress 0→1) → 3 (open)`
- Average peak gripper tip contact force: 60 – 80 N
- Press target Z offset: `+0.11 m` relative to `button_base` (fixed inside teacher).
- Includes `call_button_lit` LED observation.
- Teacher CLI:
  ```
  --approach-steps 100   --press-steps 400
  --stall-steps 20       --hold-steps 30
  --return-steps 300     --home-idle-steps 150
  --press-depth 0.035    --contact-force-threshold 0.5
  ```

### Task

- Task ID (Isaac Lab): `RobotisLab-CallButton-Right-OMY-v0`
- Robot: ROBOTIS **OMY** (6-DoF arm + 2-finger gripper)
- Pedestal: 0.908 m, gripper always closed (stiffness 2 × 10⁶, effort 5000)
- Elevator USD: random choice from 100 variants (`elevator_setup_new`)
- Robot initial pose randomization (per-seed):
  - standoff distance to button: 0.55 ~ 0.65 m
  - lateral offset: −0.05 ~ 0.05 m
  - yaw: 0°

### Cameras

All three cameras record **RGB, 1920 × 1200, Pinhole**:
- horizontal aperture 20.955 mm
- vertical aperture 13.097 mm (derived from 16:10 aspect)
- clipping (0.01, 100) m

| Name | Prim path | Frame | Position (m) | Quat (w, x, y, z) | FOV (H / V / D) | focal |
|---|---|---|---|---|---|---|
| `cam_wrist` | `Robot/OMY/link6/cam_wrist` | link6 (local, moves with arm) | (0.000, −0.080, 0.070) | (0.0018, −0.0018, 0.7071, 0.7071) | 83.21° / 58.06° / 92.64° | 11.8 mm |
| `cam_top` | `Robot/OMY/world/cam_top` | robot base | (0.000233, 0.1499, 0.43953) | (0.5144, 0.4146, −0.5064, −0.5542) | 92.67° / 66.44° / 102.03° | 10.0 mm |
| `cam_belly` | `Robot/OMY/world/cam_belly` | robot base | (0.08705, 0.1499, 0.1075) | (−0.4901, −0.5097, 0.5097, 0.4901) | 92.67° / 66.44° / 102.03° | 10.0 mm |

The `world` prim inside the OMY USD refers to the robot's base frame (link0),
**not** the simulation world.

```
world_pose(cam_top)    = robot.root_pose_w  ⊙  offset_cam_top
world_pose(cam_wrist)  = robot.root_pose_w  ⊙  FK(link0→link6)  ⊙  offset_cam_wrist
```

### HDF5 layout per episode

```
data/demo_0/
├── actions              (T, 7)    float32
├── processed_actions    (T, 10)   float32
├── obs/
│   ├── cam_wrist        (T, 1200, 1920, 3)   uint8
│   ├── cam_top          (T, 1200, 1920, 3)   uint8
│   ├── cam_belly        (T, 1200, 1920, 3)   uint8
│   ├── joint_pos                (T, 10)     float32
│   ├── joint_vel                (T, 10)     float32
│   ├── call_button_pos          (T, 3)      float32
│   ├── rel_ee_call_button_distance (T, 3)   float32
│   └── call_button_lit          (T, 1)      bool       ⭐ LED state
├── states/articulation/robot/...
├── states/rigid_object/pedestal/...
└── initial_state/...
```

### Per-version files

- `demo_XX.hdf5` — one episode per file
- `demo_XX.log` — scripted-teacher stdout with `[SETUP]`, `[PHASE]`, `[PRESSED]`, `[RESULT]` markers
- `summary.log` — one line per demo

### Suggested usage

Imitation learning (ACT, Diffusion Policy, BC) with `qpos_dim = 8`:

```python
qpos = concat(joint_pos[:6], gripper_closed_flag, call_button_lit)  # 8
action = [arm_delta[:6], gripper_cmd]                                 # 7
images = {cam_wrist, cam_top, cam_belly}                              # 1920×1200 (resize as needed)
```

For efficiency, training pipelines commonly resize to 240 × 320 before
consumption. We retain the original 1920 × 1200 to keep the data lossless.