CyberDemo: Augmenting Simulated Human Demonstration for Real-World
  Dexterous Manipulation
Authors: name: Jun Wang, name: Yuzhe Qin, name: Kaiming Kuang, name: Yigit Korkmaz, name: Akhilan Gurumoorthy, name: Hao Su, name: Xiaolong Wang

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