RoboXTechnologies/RoboX-EgoGrasp-v0.1

EgoGrasp

EgoGrasp is a crowdsourced egocentric video dataset of human grasping interactions, built for robotics imitation learning. Each clip captures a single grasp action filmed from a first-person perspective using a smartphone, covering 620+ unique everyday object categories.

What's Included Here

This repository contains a sample of 10 annotated clips from the full EgoGrasp dataset. The sample is intended to help researchers evaluate data quality, annotation depth, and compatibility with their pipelines before requesting access to the full collection.

To request access to the full dataset (1,800+ clips, 620+ object categories), visit robox.to.

Dataset Summary

• Sample clips (this repo): 10
• Full dataset: 1,800+ clips across 620+ object categories
• Perspective: First-person (egocentric), smartphone-captured
• Source: Crowdsourced via the RoboX mobile app
• Annotations: Multi-pass pipeline including hand keypoints, object bounding boxes and tracking, action segmentation, and spatial context labels

Property	Value
Total clips	10
Total duration	2 min (~0.0 hours)
Contributors	2 (anonymized)
Clips with video	10
Verified clips	10
Campaign type	ego_grasp
Export date	2026-04-08
Schema version	0.1

Collection Method

Videos are collected through the RoboX mobile app by distributed contributors following structured task prompts. Contributors record short clips of themselves picking up, holding, and placing common household and workplace objects. Quality filtering and review are applied before clips enter the annotation pipeline.

The app captures video with rich per-frame metadata including camera pose (6DoF), IMU data (200Hz), hand keypoints (21 joints), body pose, object detection, scene planes, optical flow, audio levels, navigation data, and quality metrics. On-device processing applies face detection and blurring before the video leaves the device.

Annotation Pipeline

Each clip is processed through a layered annotation pipeline:

1. Hand keypoints — 2D joint positions for both hands across all frames
2. Object detection and tracking — Bounding boxes with per-frame object identity tracking
3. Action segmentation — Temporal labels for reach, grasp, lift, hold, place, and release phases
4. Spatial context — Scene-level labels describing surface type, environment, and camera viewpoint

Use Cases

EgoGrasp is designed for researchers working on dexterous manipulation, grasp planning, hand-object interaction modeling, and policy learning from human demonstrations. The egocentric viewpoint and real-world diversity make it well suited for sim-to-real transfer and learning from unstructured environments.

Specific applications include:

• Robotic manipulation / grasping policy training via imitation learning
• Object recognition in egocentric settings
• Hand-object interaction understanding
• Benchmarking grasp detection and grip classification models

Dataset Structure

• metadata/clips.json — Per-clip metadata (device, duration, quality, contributor)
• clips/ — Video files (MP4, H.265)
• annotations/clips.jsonl — Dataset index: per-clip metadata, labels, narration, action segments, file references
• annotations/hand_keypoints/ — Per-frame hand joint positions (21 keypoints per hand, grip type)
• annotations/object_tracks/ — Per-frame detected objects with bounding boxes
• annotations/actions/ — Temporal action segments (reach, grasp, idle) derived from grip state changes
• annotations/sensors/ — Per-frame sensor data: IMU (accelerometer, gyro, magnetometer), 6DoF camera pose, camera intrinsics

Full Dataset Access

The complete EgoGrasp dataset is available upon request. Visit robox.to to learn more and submit an access request.

License

CC-BY-NC-SA-4.0 — Free for research and non-commercial use, with share-alike requirements.

Citation

If you use EgoGrasp in your research, please cite:

@dataset{robox_ego_grasp_2026,
  title={RoboX-EgoGrasp-v0.1},
  author={RoboX Team},
  year={2026},
  campaign={EgoGrasp}
}