视觉模仿实现情境化人形机器人控制
Visual Imitation Enables Contextual Humanoid Control
AI总结：
本文提出 VIDEOMIMic 真实到仿真再到真实流水线，从日常人类视频联合重建人与环境，并训练单一全身控制策略，使真实人形机器人可依据环境和根命令完成爬楼梯、坐站等情境化技能。
摘要：
我们如何教人形机器人利用周围环境情境来爬楼梯和坐在椅子上？可以说，最简单的方法就是直接向它们展示——随手拍摄一段人类运动视频，并将其提供给人形机器人。我们提出 VIDEOMIMIC，这是一条 real-to-sim-to-real（真实到仿真再到真实）流水线，可挖掘日常视频，联合重建人类与环境，并为人形机器人生成执行相应技能的全身控制策略。我们在真实人形机器人上展示了该流水线的结果，呈现出稳健、可重复的情境化控制，例如楼梯上行与下行、从椅子和长凳上坐下与站起，以及其他动态全身技能——所有这些均由单一策略完成，并以环境和全局根命令为条件。VIDEOMIMIC 为教导人形机器人在多样化真实环境中作业提供了一条可扩展路径。
英文摘要：
How can we teach humanoids to climb staircases and sit on chairs using the surrounding environment context? Arguably, the simplest way is to just show them-casually capture a human motion video and feed it to humanoids. We introduce VIDEOMIMIC, a real-to-sim-to-real pipeline that mines everyday videos, jointly reconstructs the humans and the environment, and produces whole-body control policies for humanoid robots that perform the corresponding skills. We demonstrate the results of our pipeline on real humanoid robots, showing robust, repeatable contextual control such as staircase ascents and descents, sitting and standing from chairs and benches, as well as other dynamic whole-body skills-all from a single policy, conditioned on the environment and global root commands. VIDEOMIMIC offers a scalable path towards teaching humanoids to operate in diverse real-world environments.
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。
