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Humanola Egocentric Hand-Pose Dataset — Sample Delivery

Overview

Egocentric (head-mounted) manipulation video with synchronized 3D hand-pose tracking. Per frame: 3D hand keypoints in a gravity-aligned world frame, per-joint finger angles, a grip-closure scalar, the head camera's 6-DoF world pose, and a per-hand wrist pose. Four hardware-synchronized camera streams (head stereo + both wrists) and each camera's ~200 Hz IMU accompany every episode.

Format: LeRobot v2.1.

Dataset Statistics

Metric Value
Episodes 9
Total frames 48,272 @ 30 fps
Total duration ~27 min
State-vector dimension 188
Camera streams 4 (head stereo L+R, wrist L+R), 1920×1200
IMU ~200 Hz × 3 (head + both wrists), gyro + accelerometer
Hand-tracking coverage Both hands ≈100%; absences flagged per-frame (hand_present, tracking_quality)

Head stereo: rectified, zero distortion, fx=fy≈754 px, baseline 0.120 m; wrist cameras mono; global shutter throughout. Exact per-episode camera intrinsics in meta/camera_intrinsics.json.

Episodes

# Duration Frames Task
0 4:39 8,371 ironing clothes
1 2:11 3,931 folding laundry
2 4:00 7,201 wiping
3 5:08 9,241 washing dishes
4 1:36 2,904 slicing carrots
5 2:40 4,801 peeling and slicing cucumbers
6 1:47 3,211 pouring cereal into a container
7 2:00 3,601 washing dishes
8 2:47 5,011 cutting an apple

task_index → task lookup in meta/tasks.jsonl.

Directory Structure

humanola_sample_dataset/
├── README.md
├── meta/
│   ├── info.json                   # feature shapes + per-dim column names, collection info (v2.1)
│   ├── modality.json               # observation.state field layout
│   ├── episodes.jsonl              # per-episode: length, duration, coverage, task
│   ├── episodes_stats.jsonl        # per-episode min/max/mean/std (v2.1 normalization stats)
│   ├── tasks.jsonl                 # task_index → task
│   └── camera_intrinsics.json      # per-episode intrinsics for all 4 cameras
├── data/chunk-000/
│   └── episode_000000.parquet …    # one row per timestep
├── videos/chunk-000/
│   ├── observation.images.main_stereo_left/episode_000000.mp4 …    # head stereo, LEFT eye (rendered/primary view)
│   ├── observation.images.main_stereo_right/ …                     # head stereo, RIGHT eye
│   ├── observation.images.aux_left/ …                              # left-wrist camera
│   └── observation.images.aux_right/ …                             # right-wrist camera
├── imu/
│   ├── imu_info.json               # per-sensor rate, units, camera↔IMU transform, video_key_map
│   └── chunk-000/
│       ├── main_stereo/episode_000000.parquet …   # head-cam IMU
│       ├── aux_left/episode_000000.parquet …      # left-wrist-cam IMU
│       └── aux_right/episode_000000.parquet …     # right-wrist-cam IMU
└── extras/                          # visualization aids, not loaded by LeRobot
    ├── overlays/episode_000000_overlay.mp4 …   # 2D skeleton + box on the left eye
    └── rrd/episode_000000.rrd …                # rerun: 3D world hands + wrist-cam panels

All four camera streams are hardware-synchronized (same frame count, timestamps within one frame), cut to identical per-episode ranges from t=0.

State Vector Layout

observation.state — 188 float32 per frame (offsets in meta/modality.json):

Field Indices Dims Description
left_hand_world_joints [0:63] 21×3 Left 21 joints xyz, world frame, meters
left_finger_angles [63:93] 15×2 Left finger joints × [flexion, abduction], radians
left_grip_closure [93:94] 1 Left grip-closure ∈ [0,1] (0 = open, 1 = fist)
right_hand_world_joints [94:157] 21×3 Right 21 joints xyz, world frame, meters
right_finger_angles [157:187] 15×2 Right finger joints × [flexion, abduction], radians
right_grip_closure [187:188] 1 Right grip-closure ∈ [0,1]

Joint order (21/hand — thumb cmc/mcp/ip/tip, fingers mcp/pip/dip/tip; per-dim names for every column in meta/info.json):

Index Joint Index Joint Index Joint
0 wrist 7 index_dip 14 ring_pip
1 thumb_cmc 8 index_tip 15 ring_dip
2 thumb_mcp 9 middle_mcp 16 ring_tip
3 thumb_ip 10 middle_pip 17 pinky_mcp
4 thumb_tip 11 middle_dip 18 pinky_pip
5 index_mcp 12 middle_tip 19 pinky_dip
6 index_pip 13 ring_mcp 20 pinky_tip

World frame: right-handed and gravity-aligned, in meters, with up along −Y (gravity points toward +Y). It is fixed for the whole episode and does not move with the head; its horizontal orientation is arbitrary. observation.joints_cam.* holds the same joints in the camera frame.

Finger angles & grip-closure

Finger angles are 30 values, [flexion, abduction] per joint interleaved [j0_flex, j0_abd, …], ordered level-major (proximal → distal, thumb → pinky within each level):

Slots (flex, abd) Joint Slots Joint Slots Joint
0–1 thumb_cmc 10–11 thumb_mcp 20–21 thumb_ip
2–3 index_mcp 12–13 index_pip 22–23 index_dip
4–5 middle_mcp 14–15 middle_pip 24–25 middle_dip
6–7 ring_mcp 16–17 ring_pip 26–27 ring_dip
8–9 pinky_mcp 18–19 pinky_pip 28–29 pinky_dip
  • flexion: palm-curl, radians — 0 = straight, ≈1.5 (≈90°) = bent. abduction: sideways splay, signed, ≈±0.4. (Occasional ±π = angle-wrap.)
  • grip-closure: mean flexion of the 5 MCP + 5 PIP joints ÷ 90°, clipped to [0,1]. 0 = open, 1 = fist; hand-size-independent.

Extra observation columns (not in observation.state)

Column Shape Description
observation.head_pose 7 Head-camera 6-DoF pose, world frame: [tx,ty,tz, qx,qy,qz,qw] (xyz meters, xyzw quaternion, camera→world)
observation.wrist_pose.{left,right} 7 Per-hand wrist pose, world frame, same [tx,ty,tz, qx,qy,qz,qw] layout (wrist→world)
observation.joints_cam.{left,right} 63 The 21 joints in the camera frame, meters
observation.keypoints_2d.{left,right} 42 21×2 pixel keypoints in the left-eye image
observation.bbox.{left,right} 4 Hand detection box [x1,y1,x2,y2], left-eye image
observation.hand_present.{left,right} 1 1.0 tracked, 0.0 absent
tracking_quality 1 0 = both hands good, 1 = left absent, 2 = right absent, 3 = both

When a hand is absent, its joints / keypoints / bbox / wrist_pose are zero-filled and hand_present = 0.

IMU

Each camera's ~200 Hz IMU is a separate parquet per episode at imu/chunk-000/<sensor>/episode_NNNNNN.parquet (main_stereo = head, aux_left/aux_right = wrists) — a sidecar, not a LeRobot feature, so read it directly. One row per sample:

Column Description
sample_index sample index within the file
timestamp_ns sensor timestamp on the shared camera clock (ns) — the cross-stream alignment key
timestamp_s seconds from the episode's first frame
frame fractional video-frame index; snap via round(frame) == frame_index
angular_velocity_x, _y, _z gyroscope, rad/s
linear_acceleration_x, _y, _z accelerometer, m/s² (gravity included)

imu/imu_info.json has the per-sensor rate/units, the camera↔IMU transform (camera_imu_transform, 4×4, meters), and a video_key_map (sensor → video stream; main_stereo pairs with both head eyes).

Loading Data

import json, numpy as np, pyarrow.parquet as pq

t = pq.read_table("data/chunk-000/episode_000000.parquet")
state = np.array(t.column("observation.state").to_pylist(), dtype=np.float32)   # (N, 188)

mod = json.load(open("meta/modality.json"))["state"]
def slice_(name): s = mod[name]; return state[:, s["start"]:s["end"]]

left_joints  = slice_("left_hand_world_joints").reshape(-1, 21, 3)   # (N, 21, 3) meters, world
right_joints = slice_("right_hand_world_joints").reshape(-1, 21, 3)
left_wrist   = left_joints[:, 0]                                     # (N, 3)
left_angles  = slice_("left_finger_angles")                         # (N, 30) radians
left_grip    = slice_("left_grip_closure")                          # (N, 1)

tq = np.array(t.column("tracking_quality").to_pylist())             # tq == 0 -> both hands good
# IMU: one file per sensor; align via the `frame` column
head_imu = pq.read_table("imu/chunk-000/main_stereo/episode_000000.parquet").to_pandas()
imu_at_frame_100 = head_imu[(head_imu.frame >= 99.5) & (head_imu.frame < 100.5)]

Visual checks (extras/)

  • overlays/episode_NNNNNN_overlay.mp4 — 2D keypoints + boxes on the left eye (green = left, magenta = right).
  • rrd/episode_NNNNNN.rrd — rerun: 3D world hand skeletons, head-camera frustum, wrist-camera panels.

Contact

📧 sales@humanola.com

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