MoCaP：基于被动人类视频的零样本移动操作的移动代码即策略
MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos
AI总结：
MoCaP是将被动人类视频转化为端到端移动操作策略指导的智能框架，在真实移动操作机器人的两项室内移动操作任务中各达80%成功率，优于现有方法，还提升了物体导航和可达基座位姿寻找的成功率
摘要：
日常活动的被动人类视频为机器人提供了海量人类经验，无需为每个任务单独演示。但利用这些经验需要从非结构化视频中恢复知识并适配到不同的机器人本体。为解决这些挑战，我们提出移动代码即策略（MoCaP），这是一个将被动人类视频转化为端到端移动操作策略指导的智能框架。MoCaP提取空间记忆和人类准备与物体交互的线索，为导航和寻找可操作配置提供信息。在被动人类经验和当前观测的引导下，MoCaP使用闭环代码即策略协调规划和工具使用原语，以在机器人的几何和运动学约束下完成运动。在真实移动操作机器人上，MoCaP在室内环境的两项移动操作任务中各达到80%的成功率，而现有方法无法完成该任务；视频衍生的空间记忆将物体导航成功率从70%提升至100%，人类接近先验在不利起始位置将找到可达基座位姿的比例从0%提升至100%。视频和代码可在项目网站获取：this https URL
英文摘要：
Passive human videos of everyday activity offer robots access to a massive amount of human experience without requiring a demonstration for each task. However, using this experience requires recovering knowledge from unstructured video recordings and adapting it to a different body (i.e., the robot). To overcome these challenges, we present mobile code-as-policy (MoCaP), an agentic framework that turns passive human video recordings into guidance for an end-to-end mobile manipulation policy. MoCaP extracts spatial memory and cues of how people prepare to interact with objects, which inform navigation and the search for a manipulation-ready configuration. Guided by passive human experience and current observations, MoCaP uses a closed-loop code-as-policy to coordinate planning and tool use primitives to solve motions under the robot's geometry and kinematic constraints. On a real mobile manipulator, MoCaP achieves 80% success on each of two mobile manipulation tasks in an indoor environment whereas existing approaches cannot succeed at the task. Video-derived spatial memory improves object-navigation success from 70% to 100%, and human approach prior raises the rate of finding a reachable base pose from 0% to 100% at an unfavorable start location. Videos and code are available on the project website: this https URL.
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。
