# 具身图灵机：用于机器人递归自我改进的有状态代码

> 原文链接：https://robotics.hellobike.com/papers/2026-10-08-2610-12369
> 频道：papers | 更新日期：2026-10-08 | 标签：arxiv, cs.RO
> 核心摘要：提出COAP用代码从视觉和本体感知跟踪状态并决策，在RoboDojo双臂任务上验证无测试时模型的策略。

# 具身图灵机：用于机器人递归自我改进的有状态代码

::: v-pre
> 原文链接：[Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement](https://arxiv.org/abs/2610.12369)（arXiv 2610.12369 · 发布日期：2026-10-08）

**Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement**

提出COAP用代码从视觉和本体感知跟踪状态并决策，在RoboDojo双臂任务上验证无测试时模型的策略。

## 核心要点

- 现有机器人策略多在控制环中依赖VLA或运行时查询VLM。
- COAP用代码测量并跟踪机器人、环境和任务状态，所有决策由代码完成。
- 作者分析COAP在显式状态、执行可控性与可扩展性上的优势。
- 在RoboDojo的42个双臂任务上，库在测试时无模型达到70.24%成功率。

**验证范围与局限**：验证范围为RoboDojo的42个双臂任务，摘要未说明仿真或真机。

## 英文摘要 (Abstract)

Most robot policies keep a model in the control loop: a VLA maps observations to actions, and an Agent Harness, such as Agent-as-Policy or Harness VLA queries a VLM for decision making at run time. We propose a different view: the embodied world is an Embodied Turing Machine, whose tape is the robot and environment state and rules are the policy. If this state can be represented accurately, the decision making can be written entirely in code. We therefore propose Code-Only-as-Policy (COAP): code measures and tracks the robot, environment, and task state from camera images and proprioception, and makes every decision from it. The same code applies across episodes, and different tasks share one library without a VLM or VLA in the loop. Compared with VLAs and Agent Harnesses, we analyze three advantages of COAP: (i) Explicit State: the state can be stored in code; (ii) Execution: code makes decision making controllable, recovers from failures flexibly, and runs fast and cheaply online; (iii) Extensibility: new tasks reuse, inherit, or extend the shared library, so capabilities can accumulate over tasks. These advantages make COAP a suitable medium for recursive self-improvement (RSI): coding agents develop the library in a closed loop, and each change is explicit and controllable. On RoboDojo's 42 bimanual tasks, the resulting library reaches a success rate of 70.24% without a model at test time. The upper bound of COAP lies in how accurately the state is represented for decision making and how robust the code logic is. We thus propose COAP as a new paradigm for embodied tasks; since it applies across episodes, it can also serve as an efficient data engine for VLAs and Agent Harnesses.

作者：Kairui Hu, Siyuan Hu, Fangzhou Hong, Zhaoxi Chen, Ziwei Liu
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## 原始参考来源 (Sources)

- [arXiv 2610.12369：Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement](https://arxiv.org/abs/2610.12369)