# Agent as Policy：通用智能体直接驱动机器人操作

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-11 08:00
- AIHOT 分数：47
- AIHOT 链接：https://aihot.news/items/cmu27n6l7001proveq1g5k69o
- 原文链接：https://arxiv.org/abs/2609.12541

## AI 摘要

研究者提出 Agent as Policy（AGP），让通用智能体无需任何任务或环境专属训练即可直接驱动机器人完成操作，由智能体负责任务规划与执行，解读视觉信息、编写可执行程序、发出运动指令并根据物理结果修正动作。在装配、积木搭建、骰子翻转、定向抛掷和双臂叠毛巾等真实操作任务中，AGP 在三种积木搭建配置上分别取得 100%、100% 和 80% 的成功率。

## 正文

We demonstrate that a general-purpose agent can directly drive a physical robot throughout task execution without any task-specific or environment-specific training. We introduce Agent as Policy (AGP), which places task planning and execution under the agent's control. Given a task and a robot interface, the agent interprets visual evidence, writes executable programs, issues motion commands, and revises its actions in response to physical outcomes. This brings the agent's reasoning and programming capabilities into continuous interaction with the physical world.

We study AGP across multiple real-world manipulation tasks spanning precision manipulation, dynamic motions, and deformable objects. These include assembly from human videos, block construction from goal images, die reorientation, targeted throwing, and bimanual towel folding. AGP achieves success rates of 100%, 100%, and 80% on three block construction configurations. These findings establish a path for general-purpose agents to act as robotic policies, extending their autonomy to physical manipulation through runtime reasoning, programming, and interaction.