CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation

Jun Wang, Yuzhe Qin, Kaiming Kuang, Yigit Korkmaz, Akhilan Gurumoorthy, Hao Su, Xiaolong Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 17952-17963

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/