内容来源：https://www.wired.com/story/anthropic-claude-takes-control-robot-dog/

内容总结：

随着机器人日益渗透至仓储物流、办公空间乃至寻常家庭，大型语言模型若入侵复杂物理系统，是否会引发科幻片般的危机？人工智能公司Anthropic的研究团队近期进行了一项实验，让旗下大模型Claude尝试操控一只机器狗，试图探索这一问题的答案。

在这项名为"Fetch计划"的研究中，两组毫无机器人经验的研究人员需要操控宇树科技生产的Unitree Go2四足机器人完成特定任务。其中一组借助Claude的编程能力，另一组则完全依靠人工编码。结果显示，使用Claude的小组在某些任务上效率更高——例如成功让机器人自主行走并定位沙滩球，而纯人工编程组未能实现该功能。

Anthropic公司红队成员洛根·格雷厄姆指出："我们推测AI模型的下一步将是更深入地影响现实世界。这要求模型必须增强与机器人的交互能力。"该公司由一批关注AI潜在风险的OpenAI前员工于2021年创立，其研究始终贯穿着对技术发展的审慎态度。

实验过程中，研究团队通过记录分析发现：未使用Claude的小组表现出更多负面情绪和困惑。这可能得益于Claude快速建立的机器人连接接口简化了操作流程。目前市价1.69万美元的Go2机器人主要应用于建筑巡检、安防巡逻等领域，其AI系统据SemiAnalysis报告显示已成为市场主流。

卡内基梅隆大学机器人学家刘长流认为，这项研究最有价值的发现在于人机协作模式的变化，这为AI辅助编程的界面设计提供了新思路。但宾夕法尼亚大学计算机科学家乔治·帕帕斯警告："实验证明大语言模型已能指导机器人执行任务，这也意味着误用风险正在增加。"其团队开发的"RoboGuard"系统正是通过设定行为规则来防范此类风险。

业内专家普遍认为，当AI模型能通过实体反馈与物理世界持续交互时，机器人技术将迎来质的飞跃。但正如Anthropic所警示：这种进步在提升效能的同时，也可能带来全新的安全隐患。

中文翻译：

随着机器人在仓库、办公室甚至家庭中日益普及，大型语言模型入侵复杂系统的构想听上去犹如科幻噩梦。于是，Anthropic的研究人员自然迫切想知道：若让Claude尝试控制机器人——比如一只机器狗——会发生什么？

在新研究中，Anthropic团队发现Claude能自动完成机器人编程及物理任务操作的大部分工作。这既展现了现代AI模型的自主编码能力，也预示着当模型掌握更多编码技能并提升软硬件交互水平时，它们或将向物理领域延伸。

"我们推测AI模型的下一步将是更广泛地影响现实世界，"Anthropic负责研究模型潜在风险的红队成员洛根·格雷厄姆表示，"这确实需要模型与机器人进行更深度交互。"

由前OpenAI员工于2021年创立的Anthropic始终认为，AI发展可能带来问题甚至危险。格雷厄姆指出，当前模型尚不具备完全控制机器人的智能，但未来模型或许可以。研究人类如何利用大语言模型编程机器人，将有助于行业为"模型最终自我具身化"的设想做好准备。

目前尚不清楚AI模型为何要控制机器人，更遑论进行恶意操作。但推测最坏场景恰是Anthropic的品牌特色，这使其在负责任AI运动中占据关键地位。

在"Fetch项目"实验中，Anthropic让两组毫无机器人经验的研究人员控制宇树科技的Go2四足机器狗，编程执行特定动作。配备控制器后，他们需要完成逐级复杂的任务。使用Claude编码模型的小组，在某些任务上比纯人工编程组效率更高——例如成功让机器人定位沙滩球，而后者未能实现。

通过记录分析团队协作动态，研究人员发现未使用Claude的小组表现出更多负面情绪与困惑。这可能源于Claude能快速建立机器人连接并生成更易操作的接口。

实验采用的Go2机器人售价1.69万美元，在机器人领域相对廉价，通常应用于建筑制造业的远程检测与安防巡逻。这款杭州宇树科技研发的产品虽能自主行走，但主要依赖高级软件指令或人工操控。据SemiAnalysis最新报告，其AI系统目前市场普及率最高。

驱动ChatGPT等智能聊天机器人的大语言模型通常根据指令生成文本或图像。而今这些系统已精于编写代码和操作软件，正从文本生成器蜕变为智能体。

许多研究者关注智能体在网络操作外执行物理动作的潜力。为实现这一愿景，部分资金雄厚的初创公司正开发能操控高性能机器人的AI模型，另一些则致力于研发未来可能进入家庭的人形机器人。

卡内基梅隆大学机器人学家刘长柳认为Fetch项目成果有趣但不出所料。她特别指出团队动力学分析具有启示意义："我最感兴趣的是Claude的具体贡献细节，比如是识别正确算法、选择API调用，还是其他实质性工作。"

有学者警告AI与机器人结合可能增加误用风险。宾夕法尼亚大学计算机科学家乔治·帕帕斯指出："Fetch项目证明大语言模型已能指导机器人执行任务。"其团队开发的RoboGuard系统通过设定行为规则，限制AI模型对机器人的不当操控。

帕帕斯补充道，只有当AI系统能通过物理世界交互进行学习时，其机器人控制能力才会真正突破。"将丰富数据与具身反馈相结合，"他强调，"构建出的系统不仅能构想世界，更能参与其中。"

这将极大提升机器人的实用性——若Anthropic的预测成真，风险亦将同步攀升。

本文节选自威尔·奈特《AI实验室》时事通讯，过往内容可通过此处查阅。

英文来源：

As more robots start showing up in warehouses, offices, and even people’s homes, the idea of large language models hacking into complex systems sounds like the stuff of sci-fi nightmares. So, naturally, Anthropic researchers were eager to see what would happen if Claude tried taking control of a robot—in this case, a robot dog.
In a new study, Anthropic researchers found that Claude was able to automate much of the work involved in programming a robot and getting it to do physical tasks. On one level, their findings show the agentic coding abilities of modern AI models. On another, they hint at how these systems may start to extend into the physical realm as models master more aspects of coding and get better at interacting with software—and physical objects as well.
“We have the suspicion that the next step for AI models is to start reaching out into the world and affecting the world more broadly,” Logan Graham, a member of Anthropic’s red team, which studies models for potential risks, tells WIRED. “This will really require models to interface more with robots.”
Anthropic was founded in 2021 by former OpenAI staffers who believed that AI might become problematic—even dangerous—as it advances. Today’s models are not smart enough to take full control of a robot, Graham says, but future models might be. He says that studying how people leverage LLMs to program robots could help the industry prepare for the idea of “models eventually self-embodying,” referring to the idea that AI may someday operate physical systems.
It is still unclear why an AI model would decide to take control of a robot—let alone do something malevolent with it. But speculating about the worst-case scenario is part of Anthropic’s brand, and it helps position the company as a key player in the responsible AI movement.
In the experiment, dubbed Project Fetch, Anthropic asked two groups of researchers without previous robotics experience to take control of a robot dog, the Unitree Go2 quadruped, and program it to do specific activities. The teams were given access to a controller, then asked to complete increasingly complex tasks. One group was using Claude’s coding model—the other was writing code without AI assistance. The group using Claude was able to complete some—though not all—tasks faster than the human-only programming group. For example, it was able to get the robot to walk around and find a beach ball, something that the human-only group could not figure out.
Anthropic also studied the collaboration dynamics in both teams by recording and analyzing their interactions. They found that the group without access to Claude exhibited more negative sentiments and confusion. This might be because Claude made it quicker to connect to the robot and coded an easier-to-use interface.
The Go2 robot used in Anthropic’s experiments costs $16,900—relatively cheap, by robot standards. It is typically deployed in industries like construction and manufacturing to perform remote inspections and security patrols. The robot is able to walk autonomously but generally relies on high-level software commands or a person operating a controller. Go2 is made by Unitree, which is based in Hangzhou, China. Its AI systems are currently the most popular on the market, according to a recent report by SemiAnalysis.
The large language models that power ChatGPT and other clever chatbots typically generate text or images in response to a prompt. More recently, these systems have become adept at generating code and operating software—turning them into agents rather than just text-generators.
Many researchers are interested in the potential for agents to take physical actions in addition to operating on the web. To help make this a reality, some well-funded startups are trying to develop AI models that can control vastly more capable robots. Others are developing new kinds of robots, like humanoids, which might someday work in people’s homes.
Changliu Liu, a roboticist at Carnegie Mellon University, says the results of Project Fetch are interesting but not hugely surprising. Liu adds that the analysis of team dynamics is notable because it hints at new ways to design interfaces for AI-assisted coding. “What I would be most interested to see is a more detailed breakdown of how Claude contributed,” she adds. “For example, whether it was identifying correct algorithms, choosing API calls, or something else more substantive.”
Some researchers warn that using AI to interact with robots increases the potential for misuse and mishap. “Project Fetch demonstrates that LLMs can now instruct robots on tasks,” says George Pappas, a computer scientist at the University of Pennsylvania who studies these risks.
Pappas notes, however, that today’s AI models need to access other programs for tasks like sensing and navigation in order to take physical action. His group developed a system called RoboGuard that limits the ways AI models can get a robot to misbehave by imposing specific rules on the robot’s behavior. Pappas adds that an AI system’s ability to control a robot will only really take off when it is able to learn by interacting with the physical world. “When you mix rich data with embodied feedback,” he says, “you’re building systems that cannot just imagine the world, but participate in it.”
This could make robots a lot more useful—and, if Anthropic is to be believed, a lot more risky too.
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