不用真机器人，也能训练机器人
训练机器人需要海量操作数据，但传统方式得让真人对着真实机器人一遍遍演示，费时费力还受硬件限制。这篇论文提出「数字遥操作」：用生成式世界模型替代真实机器人，操作者只需动动手（通过手部姿态流），模型就能从一张参考图合成出逼真的第一人称视频，记录下的动作序列可以迁移到任何实体机器人上。他们搭建的系统 RynnWorld-Teleop 在单张 H100 显卡上以 40+ FPS 实时生成，用这些合成数据训练的机器人能零样本从仿真迁移到真实场景，完成灵巧的双臂任务；混合真实数据后成功率还能进一步提升。它不是你明天就能用上的工具，但指向了一个未来：机器人训练数据可以像拍电影一样，在数字世界里「演」出来。
📄 原文摘要(英文)
Scaling robot learning requires massive, diverse trajectory data, yet collection is currently bottlenecked by physical teleoperation, where every demonstration binds operator time to specific hardware and workspaces. We introduce digital teleoperation, a paradigm that decouples data collection from physical constraints by replacing the real robot with a generative world model. In this framework, an operator's hand-pose stream drives a robot-centric generative world model to synthesize high-fidelity egocentric videos from a single reference image. The recorded pose stream serves as an embodiment-agnostic action label transferable to any target robot via standard retargeting, yielding complete state-action trajectories for imitation learning independent of physical hardware. We instantiate this paradigm in RynnWorld-Teleop, a system that integrates depth-aware skeletal conditioning, progressive human-to-robot training on a video Diffusion Transformer, and streaming autoregressive distillation. This pipeline compresses the generative process into a single-pass inference, enabling 40+ FPS, real-time interactive generation on a single H100 GPU. Policies trained exclusively on RynnWorld-Teleop-generated data achieve effective zero-shot Sim2Real transfer across dexterous and diverse bimanual tasks. Moreover, augmenting real-world datasets with our digitally teleoperated data consistently improves success rates, demonstrating that RynnWorld-Teleop serves as a high-fidelity, scalable data engine for the next generation of robotic agents.