AI 和人类合作开发第一个 ChatGPT 设计的机器人
Researchers have enlisted the help of ChatGPT to design and create a robot
CREATE Lab/EPFL
研究人员在ChatGPT的帮助下设计和制造了一个机器人
创建实验室/EPFL
Is there anything ChatGPT can’t do? Yes, of course, but the list appears to be getting smaller and smaller. Now, researchers have used the large language model to help them design and construct a tomato-picking robot.
有什么是ChatGPT不能做的吗？是的，当然，但名单似乎越来越少了。现在，研究人员已经使用大型语言模型来帮助他们设计和构建番茄采摘机器人。
Large language models (LLMs) can process and internalize huge amounts of text data, using this information to answer questions. OpenAI’s ChatGPT is one such LLM.
大型语言模型（LLM）可以处理和内化大量的文本数据，使用这些信息来回答问题。OpenAI的ChatGPT就是这样一个LLM。
In a new case study, researchers from the Delft University of Technology in the Netherlands and the Swiss Federal Institute of Technology (EPFL) enlisted the help of ChatGPT-3 to design and construct a robot, which might seem strange considering that ChatGPT is a language model.
在一项新的案例研究中，荷兰代尔夫特理工大学和瑞士联邦理工学院（EPFL）的研究人员在ChatGPT-3的帮助下设计和构建了一个机器人，考虑到ChatGPT是一个语言模型，这可能看起来很奇怪。
“Even though ChatGPT is a language model and its code generation is text-based, it provided significant insights and intuition for physical design, and showed great potential as a sounding board to stimulate human creativity,” said Josie Hughes, a co-author of the published case study about the experience.
“尽管ChatGPT是一种语言模型，其代码生成是基于文本的，但它为物理设计提供了重要的见解和直觉，并显示出作为激发人类创造力的传声筒的巨大潜力，”已发表的关于这种体验的案例研究的合著者乔西·休斯说。
First, the researchers asked the AI model, “What are the future challenges for humanity?” ChatGPT proposed three: food supply, an aging population and climate change. The researchers chose food supply as the most promising direction for robot design because it was outside their area of expertise.
首先，研究人员问人工智能模型，“人类未来的挑战是什么？”ChatGPT提出了三个问题：粮食供应、人口老龄化和气候变化。研究人员选择食品供应作为机器人设计最有前途的方向，因为这超出了他们的专业领域。
Using the LLM’s access to global data sourced from academic publications, technical manuals, books, and media, the researchers asked the AI what features a robot harvester should have. ChatGPT came up with a motor-driven gripper for pulling ripe tomatoes from the vine.
利用LLM对来自学术出版物、技术手册、书籍和媒体的全球数据的访问，研究人员询问人工智能机器人收割机应该具有什么功能。ChatGPT发明了一种电动抓具，用于将成熟的西红柿从藤上拽下来。
Once this general design was decided on, the researchers could move on to design specifics, including what construction materials would be used and creating computer code that would control it. Currently, LLMs can’t generate entire computer-assisted design (CAD) models, evaluate code or automatically fabricate a robot, so this step required the researchers to adopt a ‘technician’ role where they assisted with these aspects, optimizing the code written by the LLM, finalizing the CAD and fabricating the robot.
一旦确定了这一总体设计，研究人员就可以着手设计细节，包括使用什么建筑材料，以及创建控制它的计算机代码。目前，LLM无法生成整个计算机辅助设计（CAD）模型、评估代码或自动制造机器人，因此，这一步骤要求研究人员扮演“技术人员”的角色，在这些方面提供帮助，优化LLM编写的代码，完成CAD并制造机器人。
A pictorial overview of the discussion between researchers and the LLM, with the questions prompted by the human above and the options provided by the LLM below. The green shading represents the decision tree of the human, who gradually focused the problem to match their goal Stella et al./EPFL/TU Delft
研究人员和LLM之间讨论的图片概述，上面是人类提出的问题，下面是LLM提供的选项。绿色阴影代表人类的决策树，他们逐渐关注问题以达到目标 Stella等人/EPFL/TU Delft
“While computation has been largely used to assist engineers with technical implementation, for the first time, an AI system can ideate new systems, thus automating high-level cognitive tasks,” said Francesco Stella, lead author of the case study. “This could involve a shift of human roles to more technical ones.”
该案例研究的主要作者Francesco Stella表示：“虽然计算在很大程度上被用来帮助工程师进行技术实施，但人工智能系统首次可以构思新系统，从而自动化高级认知任务。”。“这可能涉及到人类角色向更技术性角色的转变。”
Based on the technical suggestions provided by ChatGPT-3, the researchers built their robot gripper and tested it in the real world, using it to pick tomatoes, which it did successfully.
根据ChatGPT-3提供的技术建议，研究人员建造了他们的机器人抓取器，并在现实世界中进行了测试，用它来采摘西红柿，并取得了成功。
a. Some of the technical suggestions generated by the LLM, including shape indications, code, component and material selection, and mechanism design. b. Guided by these inputs, a gripper was built and tested on real-world tasks, such as tomato picking, as shown at right. Stella et al./EPFL/TU Delft
a.LLM产生的一些技术建议，包括形状指示、代码、部件和材料选择以及机构设计。b.在这些输入的指导下，构建了一个夹具，并在现实世界的任务中进行了测试，例如番茄采摘，如右图所示。
Stella等人/EPFL/TU Delft
The researchers say that their case study demonstrates the potential for transforming the design process through collaboration between humans and LLMs, but they’re aware that it opens the door to varying degrees of collaboration.
研究人员表示，他们的案例研究证明了通过人类和LLM之间的合作来改变设计过程的潜力，但他们意识到这为不同程度的合作打开了大门。
At one extreme, they say, AI would act as an ‘inventor,’ providing the entirety of the robot design input with humans blindly applying it. An alternative would be to use an AI’s wide-ranging knowledge to supplement human expertise. A third approach would be to retain the human as an inventor and use AI to refine the design process through troubleshooting, debugging, and handling tedious or time-consuming processes.
他们说，在一个极端，人工智能将扮演“发明家”的角色，在人类盲目应用的情况下提供整个机器人设计输入。另一种选择是利用人工智能的广泛知识来补充人类的专业知识。第三种方法是保留人类作为发明人的身份，并使用人工智能通过故障排除、调试和处理繁琐或耗时的过程来完善设计过程。
The researchers raise ethical and commonsense risks that may result from a human-AI collaboration. They point to issues of bias, plagiarism, and intellectual property (IP) rights as areas of concern and question whether an LLM-generated design can be considered ‘novel’ given that it uses existing knowledge.
研究人员提出了人类人工智能合作可能带来的伦理和常识风险。他们指出，偏见、剽窃和知识产权问题是人们关注的领域，并质疑LLM生成的设计在使用现有知识的情况下是否可以被视为“新颖”。
“In our study, ChatGPT identified tomatoes as the crop ‘most worth’ pursuing for a robotic harvester," Hughes said. "However, this may be biased towards crops that are more covered in literature, as opposed to those where there is truly a real need. When decisions are made outside the scope of knowledge of the engineer, this can lead to significant ethical, engineering, or factual errors.”
Hughes说：“在我们的研究中，ChatGPT将番茄确定为机器人收割机‘最值得’追求的作物。然而，这可能偏向于文献中涵盖更多的作物，而不是真正需要的作物。当决策超出了工程师的知识范围时，这可能会导致重大的道德、工程或事实错误。”
Despite these concerns, the researchers believe there is great potential in human-AI collaboration if it’s well managed.
尽管存在这些担忧，研究人员认为，如果管理得当，人类与人工智能的合作仍有巨大潜力。
“The robotics community must identify how to leverage these powerful tools to accelerate the advancement of robots in an ethical, sustainable and socially empowering way,” the researchers said. “Looking forward, we strongly believe that LLMs will open up many exciting possibilities and that, if opportunely managed, they will be a force for good.”
研究人员表示：“机器人界必须确定如何利用这些强大的工具，以道德、可持续和社会赋权的方式加速机器人的发展。” “展望未来，我们坚信LLM将开辟许多令人兴奋的可能性，如果管理得当，它们将成为一股有益的力量。”
The case study was published in the journal Nature Machine Intelligence.
该案例研究发表在《自然机器智能》杂志上。
Source: EPFL
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