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

翻译：摘要：我们提出CyberDemo，一种利用模拟人类演示处理现实世界任务的机器人模仿学习新方法。通过在模拟环境中融入大量数据增强，CyberDemo在迁移至现实世界时超越了传统的同领域现实演示方法，能够适应多样化的物理与视觉条件。尽管其数据采集成本低廉且便捷，CyberDemo在各类任务的成功率上仍优于基线方法，并展现出对未见过物体的泛化能力。例如，尽管人类演示仅包含三阀体，CyberDemo仍可旋转新型四阀体与五阀体。本研究证明了模拟人类演示在现实世界灵巧操控任务中的巨大潜力。更多细节请访问 https://cyber-demo.github.io