Acquiring large-scale, high-fidelity robot demonstration data remains a critical bottleneck for scaling Vision-Language-Action (VLA) models in dexterous manipulation. We propose a Real-Sim-Real data collection and data editing pipeline that transforms human demonstrations into robot-executable, environment-specific training data without direct robot teleoperation. Standardized data collection rooms are built to capture multimodal human demonstrations (synchronized 3 RGB-D videos, 11 RGB videos, 29-DoF glove joint angles, and 14-channel tactile signals). Based on these human demonstrations, we introduce a tactile-aware retargeting method that maps human hand states to robot dex-hand states via geometry and force-guided optimization. Then the retargeted robot trajectories are rendered in a photorealistic Isaac Sim environment to build robot training data. Real world experiments have demonstrated: (1) The retargeted dex-hand trajectories achieve an 84\% success rate across 10 diverse object manipulation tasks. (2) VLA policies (Pi0.5) trained exclusively on our generated data achieve 80\% average success rate on three representative tasks, i.e., pick-and-place, pushing and pouring. To conclude, robot training data can be efficiently "painted" from human demonstrations using our real-sim-real data pipeline. We offer a scalable, cost-effective alternative to teleoperation with minimal performance loss for complex dexterous manipulation.

翻译：获取大规模、高保真的机器人演示数据，仍然是扩展视觉-语言-动作（VLA）模型在灵巧操作中应用的关键瓶颈。我们提出了一种“真实-仿真-真实”数据收集与数据编辑流程，该流程将人类演示转化为机器人可执行、环境特定的训练数据，而无需直接的机器人遥操作。我们构建了标准化的数据采集室，以捕获多模态的人类演示数据（同步的3个RGB-D视频、11个RGB视频、29自由度手套关节角度以及14通道触觉信号）。基于这些人类演示，我们引入了一种触觉感知的重定向方法，该方法通过几何与力引导的优化，将人手状态映射到机器人灵巧手状态。随后，重定向后的机器人轨迹在照片级逼真的Isaac Sim仿真环境中进行渲染，以构建机器人训练数据。真实世界实验表明：（1）重定向后的灵巧手轨迹在10种不同的物体操作任务中实现了84%的成功率。（2）仅使用我们生成的数据训练的VLA策略（Pi0.5）在三个代表性任务（即拾取与放置、推动和倾倒）上实现了80%的平均成功率。总而言之，利用我们的真实-仿真-真实数据流程，可以高效地从人类演示中“绘制”出机器人训练数据。我们为复杂的灵巧操作提供了一种可扩展、成本效益高的遥操作替代方案，且性能损失最小。