模仿有效的方法：基于仿真过滤的人类视频模块化策略学习
Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos
AI总结：
提出Perceive-Simulate-Imitate框架，通过仿真过滤人类视频中的抓取-轨迹对，学习任务导向的抓取与后抓取运动策略，无需机器人数据即可实现鲁棒操作。
摘要：
通过观看人类视频学习操作技能的能力有潜力为机器人学习解锁新的高度可扩展数据源。本文研究抓取操作，其中任务涉及在抓取物体后执行各种后抓取运动。人类视频为学习后抓取运动提供了强信号，但对于学习先决的抓取行为帮助较小，尤其是对于没有类人手的机器人。一个有前景的方法是采用模块化策略设计，利用专用抓取生成器产生稳定抓取。然而，任意稳定抓取通常与任务不兼容，阻碍机器人执行期望的下游运动。为解决这一挑战，我们提出Perceive-Simulate-Imitate (PSI)框架，该框架使用通过仿真中配对抓取-轨迹过滤处理的人类视频运动数据来训练模块化操作策略。这一仿真步骤用抓取适用性标签扩展轨迹数据，从而允许对任务导向的抓取能力进行监督学习。通过真实世界实验，我们展示了该框架可以在没有任何机器人数据的情况下高效学习精确操作技能，相比直接使用抓取生成器，性能显著更鲁棒。
英文摘要：
The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning. Here, we tackle prehensile manipulation, in which tasks involve grasping an object before performing various post-grasp motions. Human videos offer strong signals for learning the post-grasp motions, but they are less useful for learning the prerequisite grasping behaviors, especially for robots without human-like hands. A promising way forward is to use a modular policy design, leveraging a dedicated grasp generator to produce stable grasps. However, arbitrary stable grasps are often not task-compatible, hindering the robot's ability to perform the desired downstream motion. To address this challenge, we present Perceive-Simulate-Imitate (PSI), a framework for training a modular manipulation policy using human video motion data processed by paired grasp-trajectory filtering in simulation. This simulation step extends the trajectory data with grasp suitability labels, which allows for supervised learning of task-oriented grasping capabilities. We show through real-world experiments that our framework can be used to learn precise manipulation skills efficiently without any robot data, resulting in significantly more robust performance than using a grasp generator naively.
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。
