RynnWorld-Teleop：面向数字遥操作的动作条件世界模型

摘要

扩展机器人学习规模需要海量、多样化的轨迹数据，然而当前数据收集受限于物理遥操作——每次演示都将操作员的时间与特定硬件和工作空间绑定。我们提出数字遥操作这一新范式，通过用生成式世界模型替代真实机器人，将数据收集与物理约束解耦。在该框架中，操作员的手部姿态流驱动以机器人为中心的生成式世界模型，从单张参考图像合成高保真第一人称视频。记录的姿态流作为与具身无关的动作标签，可通过标准重定向迁移至任意目标机器人，从而产生独立于物理硬件的完整状态-动作轨迹，用于模仿学习。我们以RynnWorld-Teleop系统实例化该范式，该系统集成了深度感知骨骼条件化、渐进式人-机器人训练方案（基于视频扩散变换器）以及流式自回归蒸馏。该流水线将生成过程压缩为单次推理，在单块H100 GPU上实现40帧/秒以上的实时交互式生成。完全基于RynnWorld-Teleop生成数据训练的策略，在灵巧且多样的双手任务中实现了有效的零样本Sim2Real迁移。此外，将我们的数字遥操作数据增强到真实世界数据集，持续提升了任务成功率，证明RynnWorld-Teleop可作为面向下一代机器人体的高保真、可扩展数据引擎。

English

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.