---
license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - lerobot
  - egocentric
  - manipulation
  - bimanual
  - hand-pose
  - MANO
  - 3d-reconstruction
  - world-model
  - VLA
  - imitation-learning
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/chunk-000/file-000.parquet
language:
  - en
  - zh
pretty_name: "EgoWorld: Egocentric Bimanual Manipulation with 3D World-Frame Hand Poses"
size_categories:
  - n<1K
---

# EgoWorld: Egocentric Bimanual Manipulation with 3D World-Frame Hand Poses

A [LeRobot v3.0](https://huggingface.co/docs/lerobot/lerobot-dataset-v3) dataset of egocentric bimanual manipulation from human demonstrations, featuring **world-frame 3D hand poses**, **full MANO hand meshes**, **camera trajectories**, and **dense depth maps**.

Designed for training **Vision-Language-Action (VLA) models**, **world models**, and **imitation learning** policies from human hand demonstrations.

## Key Features

| Feature | Description |
|---------|-------------|
| **World-frame hand poses** | 6 keypoints per hand (wrist + 5 fingertips) in metric 3D world coordinates |
| **Full MANO mesh** | 778 vertices per hand per frame for detailed hand shape |
| **Bimanual actions** | 40D world-frame delta actions (both hands + gripper openness) |
| **Camera tracking** | 6-DOF camera-to-world SE(3) poses |
| **Dense depth** | Per-frame metric depth maps + colorized depth video |
| **Gripper proxy** | Thumb-index distance as grasp openness signal |

## Dataset Summary

| Property | Value |
|----------|-------|
| Episodes | 2 |
| Total Frames | 514 |
| FPS | 10 Hz |
| State Dim | 40 (world-frame bimanual hand) |
| Action Dim | 40 (world-frame hand delta) |
| Hand Model | MANO (778 vertices, 1538 faces) |
| Camera | Egocentric (head-mounted) |
| RGB Resolution | 480 x 640 |
| Depth Resolution | 384 x 512 |

### Episodes

| # | Task | Frames | Duration |
|---|------|--------|----------|
| 0 | Tidy the desk by organizing and rearranging items | 220 | 22s |
| 1 | Fold and tidy clothes on the table | 294 | 29.4s |

## State and Action Representation

```
observation.state / action — 40D float32, world frame
┌──────────── Left Hand (20D) ──────────────┐
│  [0:3]   wrist xyz (meters)               │
│  [3:6]   thumb tip xyz                    │
│  [6:9]   index finger tip xyz             │
│  [9:12]  middle finger tip xyz            │
│  [12:15] ring finger tip xyz              │
│  [15:18] pinky finger tip xyz             │
│  [18]    gripper openness (thumb-index m) │
│  [19]    detection confidence [0,1]       │
├──────────── Right Hand (20D) ─────────────┤
│  [20:38] (same layout as left)            │
│  [38]    gripper openness                 │
│  [39]    detection confidence             │
└───────────────────────────────────────────┘
action[t] = state[t+1] - state[t]
```

## Quick Start

```python
import pyarrow.parquet as pq
import numpy as np

data = pq.read_table("data/chunk-000/file-000.parquet")

# World-frame hand state and action
state = np.array(data["observation.state"][50].as_py())   # (40,)
action = np.array(data["action"][50].as_py())             # (40,)

# World-frame hand keypoints (6 points x 3D per hand)
left_kps = np.array(data["observation.hand_left_keypoints_world"][50].as_py()).reshape(6, 3)
right_kps = np.array(data["observation.hand_right_keypoints_world"][50].as_py()).reshape(6, 3)

# Camera-to-world pose
c2w = np.array(data["observation.camera_pose"][50].as_py()).reshape(4, 4)

# Full MANO mesh vertices (778 x 3)
mesh = np.array(data["observation.hand_left_vertices_camera"][50].as_py()).reshape(778, 3)
```

## All Features (28 columns)

| Column | Shape | Frame | Description |
|--------|-------|-------|-------------|
| `observation.state` | (40,) | World | Bimanual hand pose |
| `action` | (40,) | World | Hand pose delta |
| `observation.camera_pose` | (16,) | World | Flattened c2w 4x4 SE(3) |
| `observation.camera_intrinsics` | (4,) | Pixels | [fx, fy, cx, cy] |
| `observation.camera_translation` | (3,) | World | Camera position (m) |
| `observation.camera_rotation_quat` | (4,) | World | Quaternion (w,x,y,z) |
| `observation.hand_{L/R}_keypoints_world` | (18,) | World | 6 keypoints x 3D |
| `observation.hand_{L/R}_keypoints_camera` | (18,) | Camera | 6 keypoints local |
| `observation.hand_{L/R}_vertices_camera` | (2334,) | Camera | Full MANO mesh |
| `observation.hand_{L/R}_cam_t` | (3,) | Camera | Hand 3D translation |
| `observation.hand_{L/R}_bbox` | (4,) | Pixels | [x1,y1,x2,y2] |
| `observation.hand_{L/R}_gripper` | (1,) | World | Thumb-index distance |
| `observation.hand_{L/R}_confidence` | (1,) | — | Detection score |
| `observation.hand_{L/R}_depth` | (1,) | Camera | Depth at hand (m) |
| `episode_index` | int64 | — | Episode ID |
| `frame_index` | int64 | — | Frame within episode |
| `timestamp` | float32 | — | Time (seconds) |
| `index` | int64 | — | Global frame index |
| `task_index` | int64 | — | Task ID |
| `next.done` | bool | — | Episode end flag |

## Coordinate Frames

All hand keypoints in `observation.state` and `hand_{L/R}_keypoints_world` are in a **fixed world frame** (Y-down OpenCV convention). Hand mesh vertices in `hand_{L/R}_vertices_camera` are in the hand detector's local camera frame. To reconstruct the full mesh in 3D camera coordinates: `mesh_3d = vertices + cam_t`.

**World-frame projection**: 2D hand keypoints are projected onto the depth map, backprojected to 3D using camera intrinsics, then transformed to world frame via the camera-to-world pose.

## Data Quality

| Metric | Value |
|--------|-------|
| Left hand detection rate | 98.6% |
| Right hand detection rate | 100% |
| Wrist-to-camera distance | 0.3 - 1.0 m |
| Gripper openness range | 0.00 - 0.31 m |
| Action magnitude (mean) | 0.035 m/frame |

## Citation

```bibtex
@misc{egoworld2026,
  title={EgoWorld: Egocentric Bimanual Manipulation with 3D World-Frame Hand Poses},
  author={Haoyang Li},
  year={2026},
  note={LeRobot v3.0 format}
}
```