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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.

sourceHugging Faceapache-2.0updated 24d agoView on Hugging Face
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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

bibtex
@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