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

1UC San Diego; 2USC
CVPR 2024

*Indicates Equal Contribution

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.


                Video Presentation



CyberDemo Pipeline. First, we collect both simulated and real demonstrations via vision-based teleoperation. Following this, we train the policy on simulated data, incorporating the proposed data augmentation techniques. During training, we apply automatic curriculum learning, which incrementally enhances the randomness scale based on task performance. Finally, the policy is fine-tuned with a few real demos before being deployed to the real world.

Real World Deployments

Pouring Task

Rotating Task

Pick And Place Task


        author    = {Wang, Jun and Qin, Yuzhe and Kuang, Kaiming and Korkmaz, Yigit and Gurumoorthy, Akhilan and Su, Hao and Wang, Xiaolong},
        title     = {{CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation}},
        journal   = {arXiv preprint arXiv: 2312.09237},
        year      = {2024},