title: CyberDemo: Augmenting Simulated Human Demonstration for Real-World
    Dexterous Manipulation
summary: We introduce CyberDemo, a novel approach to robotic imitation learning that
  leverages simulated human demonstrations for real-world tasks. By incorporating
  extensive data augmentation in a simulated environment, CyberDemo outperforms
  traditional in-domain real-world demonstrations when transferred to the real
  world, handling diverse physical and visual conditions. Regardless of its
  affordability and convenience in data collection, CyberDemo outperforms
  baseline methods in terms of success rates across various tasks and exhibits
  generalizability with previously unseen objects. For example, it can rotate
  novel tetra-valve and penta-valve, despite human demonstrations only involving
  tri-valves. Our research demonstrates the significant potential of simulated
  human demonstrations for real-world dexterous manipulation tasks. More details
  can be found at https://cyber-demo.github.io
paper_authors._id: 65d82591a5269668b716fcfa
paper_authors.name: Jun Wang
paper_authors._id: 65d82591a5269668b716fcfb
paper_authors.name: Yuzhe Qin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-23T13:13:06.224Z
paper_authors._id: 64ad64c2c1da7c4dbc7b19c5
paper_authors.avatarUrl: /avatars/98efbab6c65405e8701c64c290a7a87c.svg
paper_authors.fullname: Yuzhe Qin
paper_authors.type: user
paper_authors.user: yuzheqin
paper_authors._id: 65d82591a5269668b716fcfc
paper_authors.name: Kaiming Kuang
paper_authors._id: 65d82591a5269668b716fcfd
paper_authors.name: Yigit Korkmaz
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-24T14:37:02.812Z
paper_authors._id: 65d8e45d0884c37e111d5ee0
paper_authors.avatarUrl: /avatars/989d9d6caf0f6adb4c8297125a0b86ad.svg
paper_authors.fullname: Yigit Korkmaz
paper_authors.type: user
paper_authors.user: ykorkmaz
paper_authors._id: 65d82591a5269668b716fcfe
paper_authors.name: Akhilan Gurumoorthy
paper_authors._id: 65d82591a5269668b716fcff
paper_authors.name: Hao Su
paper_authors._id: 65d82591a5269668b716fd00
paper_authors.name: Xiaolong Wang
paper_title: CyberDemo: Augmenting Simulated Human Demonstration for Real-World
    Dexterous Manipulation
paper_summary: We introduce CyberDemo, a novel approach to robotic imitation learning that
  leverages simulated human demonstrations for real-world tasks. By incorporating
  extensive data augmentation in a simulated environment, CyberDemo outperforms
  traditional in-domain real-world demonstrations when transferred to the real
  world, handling diverse physical and visual conditions. Regardless of its
  affordability and convenience in data collection, CyberDemo outperforms
  baseline methods in terms of success rates across various tasks and exhibits
  generalizability with previously unseen objects. For example, it can rotate
  novel tetra-valve and penta-valve, despite human demonstrations only involving
  tri-valves. Our research demonstrates the significant potential of simulated
  human demonstrations for real-world dexterous manipulation tasks. More details
  can be found at https://cyber-demo.github.io
paper_ai_summary: CyberDemo leverages augmented simulated human demonstrations to enhance real-world robotic imitation learning, improving success rates and generalizability across diverse tasks and objects.
paper_ai_keywords: robotic imitation learning simulated human demonstrations data augmentation generalizability dexterous manipulation tasks