# 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：https://robotics.hellobike.com/papers/2026-10-14-2610-20998
> 频道：papers | 更新日期：2026-10-14 | 标签：arxiv, cs.RO
> 核心摘要：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。

# MoCaP：用于从被动人类视频进行零样本移动操作的 Mobile Code-as-Policies

::: v-pre
> 原文链接：[MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos](https://arxiv.org/abs/2610.20998)（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
:::

## 原始参考来源 (Sources)

- [arXiv 2610.20998：MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos](https://arxiv.org/abs/2610.20998)