introvoyz041/ego2robot-factory-episodes
Ego2Robot: Factory Manipulation Episodes Dataset Description 50 curated episodes of factory worker manipulation tasks, converted from egocentric video into LeRobot-compatible format for robot learning research. Key Features 50 episodes (~1,800 frames total) Real factory work from 85 manufacturing facilities 10 skill clusters discovered via unsupervised learning LeRobot v3.0 format with observations + pseudo-actions Rich annotations: VideoMAE… See the full description on the dataset page: https://huggingface.co/datasets/introvoyz041/ego2robot-factory-episodes.
Ego2Robot: Factory Manipulation Episodes
Dataset Description
50 curated episodes of factory worker manipulation tasks, converted from egocentric video into LeRobot-compatible format for robot learning research.
Key Features
- 50 episodes (~1,800 frames total)
- Real factory work from 85 manufacturing facilities
- 10 skill clusters discovered via unsupervised learning
- LeRobot v3.0 format with observations + pseudo-actions
- Rich annotations: VideoMAE embeddings, CLIP labels, quality scores
Data Structure
Each episode contains:
- Observations:
observation.images.top: RGB frames (360x640, 6fps)observation.state: Hand bounding box [xmin, ymin, xmax, ymax]- Actions: 2D hand motion vectors
[delta_x, delta_y](pseudo-actions for representation learning) - Metadata: Skill cluster ID, zero-shot action label, quality scores
Skill Distribution
- Quality Inspection: 50% (25 episodes)
- Assembly: 17% (9 episodes)
- Fastening: 17% (8 episodes)
- Machine Operation: 8% (4 episodes)
- Mixed: 8% (4 episodes)
Intended Use
Primary: Representation learning and pretraining for vision-language-action (VLA) models
- Pretrain visual encoders on diverse manipulation tasks
- Learn spatial reasoning from egocentric perspective
- Discover manipulation primitives via clustering
- Domain adaptation for manufacturing robotics
NOT intended for: Direct robot policy learning (actions are pseudo-actions from human hand motion, not robot joint commands)
Data Collection
- Source: BuildAI Egocentric-10K dataset
- Processing pipeline:
- Quality filtering (motion + hand visibility)
- VideoMAE embeddings (768-dim)
- CLIP zero-shot labeling
- K-means clustering (10 skills)
- Hand tracking (MediaPipe)
- LeRobot format conversion
Citation
@dataset{ego2robot2025,
title={Ego2Robot: Factory Manipulation Episodes for Robot Learning},
author={Michelle Sun},
year={2025},
url={https://huggingface.co/datasets/msunbot1/ego2robot-factory-episodes}
}License
Apache 2.0 (inherits from Egocentric-10K source dataset)
Contact
For questions or collaboration: x.com/michellelsun
