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Datasets:
RobotisAI
/
evButtonPush-260423-01-hdf5

Tasks:
Robotics
Size:
10B<n<100B
Tags:
robotics
manipulation
imitation-learning
elevator-button-press
isaac-sim
isaac-lab
License:
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Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
                  import h5py
              ModuleNotFoundError: No module named 'h5py'
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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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 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:

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 resize해서 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.

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:

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.
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