EgoAVFlow：通过人类中心视频主动视觉实现机器人策略学习
EgoAVFlow: Robot Policy Learning with Active Vision from Human Egocentric Videos via 3D Flow
AI总结：
EgoAVFlow通过共享3D流表示从人类中心视频中学习机器人策略，实现主动视觉和稳健操纵，无需机器人演示。
摘要：
人类中心视频提供了一种可扩展的操纵演示来源；然而，将其部署到机器人上需要主动视角控制以保持任务关键的可见性，而人类视角模仿往往由于人类特定的先验知识而无法提供。我们提出了EgoAVFlow，它通过共享的3D流表示从人类中心视频中学习操纵和主动视觉，该表示支持几何可见性推理并能够在没有机器人演示的情况下进行转移。EgoAVFlow使用扩散模型来预测机器人动作、未来3D流和相机轨迹，并在测试时通过基于预测运动和场景几何计算的可见性感知奖励的最大化去噪来优化视角。在主动变化的视角下的现实世界实验表明，EgoAVFlow在没有机器人演示的情况下一致优于先前的人类演示基线，展示了有效的可见性维护和稳健的操纵。
英文摘要：
Egocentric human videos provide a scalable source of manipulation demonstrations; however, deploying them on robots requires active viewpoint control to maintain task-critical visibility, which human viewpoint imitation often fails to provide due to human-specific priors. We propose EgoAVFlow, which learns manipulation and active vision from egocentric videos through a shared 3D flow representation that supports geometric visibility reasoning and transfers without robot demonstrations. EgoAVFlow uses diffusion models to predict robot actions, future 3D flow, and camera trajectories, and refines viewpoints at test time with reward-maximizing denoising under a visibility-aware reward computed from predicted motion and scene geometry. Real-world experiments under actively changing viewpoints show that EgoAVFlow consistently outperforms prior human-demo-based baselines, demonstrating effective visibility maintenance and robust manipulation without robot demonstrations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
