CRAFT：基于视频扩散的双臂机器人数据生成
CRAFT: Video Diffusion for Bimanual Robot Data Generation
AI总结：
CRAFT通过视频扩散生成双臂机器人演示数据，利用边缘结构线索提升生成多样性与鲁棒性，提升多视角任务的泛化能力。
摘要：
双臂机器人通过示范学习受限于现实数据成本和视觉多样性，限制了策略在不同视角、物体配置和形态上的鲁棒性。我们提出了Canny引导的机器人数据生成方法，利用视频扩散变换器生成时间连贯的操纵视频并生成动作标签。通过将边缘结构线索从模拟轨迹中提取并条件化视频扩散，CRAFT生成物理合理的轨迹变化，并支持跨越物体姿态、相机视角、光照和背景变化的统一增强管道。我们利用预训练的视频扩散模型将模拟视频及模拟轨迹的动作标签转换为动作一致的演示。从少量现实演示开始，CRAFT生成大量视觉多样化的逼真训练数据，无需在真实机器人上回放演示（Sim2Real）。在模拟和现实双臂任务中，CRAFT在现有增强策略和简单数据扩展上提升了成功率，证明基于扩散的视频生成能显著扩展演示多样性并提高双臂操作任务的泛化能力。项目网站：https://craftaug.github.io/
英文摘要：
Bimanual robot learning from demonstrations is fundamentally limited by the cost and narrow visual diversity of real-world data, which constrains policy robustness across viewpoints, object configurations, and embodiments. We present Canny-guided Robot Data Generation using Video Diffusion Transformers (CRAFT), a video diffusion-based framework for scalable bimanual demonstration generation that synthesizes temporally coherent manipulation videos while producing action labels. By conditioning video diffusion on edge-based structural cues extracted from simulator-generated trajectories, CRAFT produces physically plausible trajectory variations and supports a unified augmentation pipeline spanning object pose changes, camera viewpoints, lighting and background variations, cross-embodiment transfer, and multi-view synthesis. We leverage a pre-trained video diffusion model to convert simulated videos, along with action labels from the simulation trajectories, into action-consistent demonstrations. Starting from only a few real-world demonstrations, CRAFT generates a large, visually diverse set of photorealistic training data, bypassing the need to replay demonstrations on the real robot (Sim2Real). Across simulated and real-world bimanual tasks, CRAFT improves success rates over existing augmentation strategies and straightforward data scaling, demonstrating that diffusion-based video generation can substantially expand demonstration diversity and improve generalization for dual-arm manipulation tasks. Our project website is available at: https://craftaug.github.io/
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