外观
论文arXiv 2610.2099814 天前
MoCaP：用于从被动人类视频进行零样本移动操作的 Mobile Code-as-Policies
原文链接：MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos（arXiv 2610.20998 · 发布日期：2026-10-14）
MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos
针对从被动人类视频迁移经验到机器人移动操作的问题，MoCaP生成策略指导，并在真实室内任务验证。
核心要点
- 动机：日常人类视频蕴含经验，但需从非结构化录像中恢复知识并适配机器人身体。
- 方法：提取空间记忆和人类准备交互线索，引导导航与可操作构型搜索。
- 策略：闭环 code-as-policy 协调规划和工具原语，满足机器人几何与运动学约束。
- 实验：真实平台两项室内任务各达80%成功；项目网站提供视频和代码。
验证范围与局限：仅在一台真实移动操作臂的室内两项移动操作任务上验证。
英文摘要 (Abstract)
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: https://mocap-7.github.io/MoCAP-page/.
作者：Yiyang Cai, Tatsuya Kamijo, Ninghao Lu, Zijin Hu, Sherry Yang
来源
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