TechCrunch News 2024年10月16日
Boston Dynamics teams with TRI to bring AI smarts to Atlas humanoid robot
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波士顿动力与丰田研究院(TRI)宣布合作,将基于AI的机器人智能应用于电动Atlas人形机器人。此次合作将利用TRI在大型行为模型(LBMs)方面的工作成果,LBMs与ChatGPT等平台背后的更常见的大型语言模型(LLMs)运作方式类似。TRI一直在研究如何让机器人通过一夜训练在执行翻煎饼等家务时达到90%的准确率。波士顿动力在硬件方面与TRI十分契合,该公司在软件和AI方面也做了不少工作,为其自身系统提供动力,但教机器人自主执行复杂任务所需的努力方式完全不同。波士顿动力在4月份发布了电动Atlas的设计,最终取代了其更大、液压驱动的同类产品。尽管此后我们对这款机器人的了解很少,但在8月份,TechCrunch获得了一段简短的视频,展示了这款机器人做俯卧撑。与Atlas最初的视频一样,这个快速的俯卧撑演示很好地展示了这款机器人的非凡力量。波士顿动力在人形机器人领域的主要竞争对手,包括Agility、Figure和特斯拉,主要选择内部组建AI团队。考虑到这两家机构分别由现代和丰田运营——汽车领域的直接竞争对手,因此波士顿动力与TRI的合作尤其引人注目。与此同时,波士顿动力拥有自己的研究衍生机构,即AI研究院(前身为波士顿动力AI研究院)。尽管由波士顿动力创始人兼前首席执行官马克·雷伯特领导,但该研究院保持着独立于波士顿动力。它也是一家规模较小的机构,仍在组建团队。TRI则越来越少地投入到硬件方面。所有这些努力的目标都是创造一台真正的通用机器。也就是说,一个能够学习并完成人类所能做的一切(甚至更多)的系统。虽然我们已经看到机器人硬件发展到接近这种复杂程度,但接近通用智能的东西则要难得多。当然,系统SDK的出现极大地扩展了机器人(如波士顿动力Spot)能够执行的任务范围,但真正的通用人工智能还有很长的路要走,如果我们能做到的话。

🤖 波士顿动力与丰田研究院(TRI)宣布合作,将基于AI的机器人智能应用于电动Atlas人形机器人。此次合作将利用TRI在大型行为模型(LBMs)方面的工作成果,LBMs与ChatGPT等平台背后的更常见的大型语言模型(LLMs)运作方式类似。

🤖 TRI一直在研究如何让机器人通过一夜训练在执行翻煎饼等家务时达到90%的准确率。波士顿动力在硬件方面与TRI十分契合,该公司在软件和AI方面也做了不少工作,为其自身系统提供动力,但教机器人自主执行复杂任务所需的努力方式完全不同。

🤖 波士顿动力在4月份发布了电动Atlas的设计,最终取代了其更大、液压驱动的同类产品。尽管此后我们对这款机器人的了解很少,但在8月份,TechCrunch获得了一段简短的视频,展示了这款机器人做俯卧撑。与Atlas最初的视频一样,这个快速的俯卧撑演示很好地展示了这款机器人的非凡力量。

🤖 波士顿动力在人形机器人领域的主要竞争对手,包括Agility、Figure和特斯拉,主要选择内部组建AI团队。考虑到这两家机构分别由现代和丰田运营——汽车领域的直接竞争对手,因此波士顿动力与TRI的合作尤其引人注目。

🤖 所有这些努力的目标都是创造一台真正的通用机器。也就是说,一个能够学习并完成人类所能做的一切(甚至更多)的系统。虽然我们已经看到机器人硬件发展到接近这种复杂程度,但接近通用智能的东西则要难得多。当然,系统SDK的出现极大地扩展了机器人(如波士顿动力Spot)能够执行的任务范围,但真正的通用人工智能还有很长的路要走,如果我们能做到的话。

Boston Dynamics and Toyota Research Institute (TRI) Wednesday revealed plans to bring AI-based robotic intelligence to the electric Atlas humanoid robot. The collaboration will leverage the work that TRI has done around large behavior models (LBMs), which operate along similar lines as the more familiar large language models (LLMs) behind platforms like ChatGPT.

Last September, TechCrunch paid a visit to TRI’s Bay Area campus for a closer look at the institute’s work on robot learning. In research revealed at last year’s Disrupt conference, institute head Gill Pratt explained how the lab has been able to get robots to 90% accuracy when performing household tasks like flipping pancakes through overnight training.

“In machine learning, up until quite recently there was a tradeoff, where it works, but you need millions of training cases,” Pratt explained at the time. “When you’re doing physical things, you don’t have time for that many, and the machine will break down before you get to 10,000. Now it seems that we need dozens. The reason for the dozens is that we need to have some diversity in the training cases. But in some cases, it’s less.”

Boston Dynamics is a good match for TRI on the hardware side. The Spot-maker has done its share on the software and AI front to power its own systems, but the manner of work required to teach robots to perform complex tasks with full autonomy is another beast altogether.

Gill Pratt speaks at TC Sessions: Robotics 2017Image Credits:TechCrunch

“There has never been a more exciting time for the robotics industry, and we look forward to working with TRI to accelerate the development of general-purpose humanoids,” Boston Dynamics CEO Robert Playter notes in a statement. “This partnership is an example of two companies with a strong research-and-development foundation coming together to work on many complex challenges and build useful robots that solve real-world problems.”

Boston Dynamics revealed its design for the electric Atlas in April, as it finally put to rest the humanoid’s larger, hydraulic namesake. While we’ve seen very little of the robot since then, in August, TechCrunch managed to get its hands on a short video of the robot doing pushups. Like Atlas’ initial video, the quick pushup demo was a good demonstration of the robot’s remarkable strength.

Boston Dynamics’ chief competition in the humanoid robot space, including Agility, Figure, and Tesla, have primarily opted to build out their AI teams in-house. The Boston Dynamics-TRI deal is especially interesting given that the organizations are run by Hyundai and Toyota – direct competitors in the automotive space.

Image Credits:Boston Dynamics

Meanwhile, Boston Dynamics has its own research spinout, The AI Institute (formerly The Boston Dynamics AI Institute). Though run by Boston Dynamics founder and former CEO, Marc Raibert, the institute maintains independence from Boston Dynamics, proper. It’s also a significantly younger organization still in the process of building out its team. TRI, for its part, has become less invested in the hardware side of the equation.

The goal in all of this is a true general-purpose machine. That is to say, a system that is essentially capable of learning and doing all of the things a person can do – and, presumably, more. While we’ve seen robot hardware evolve closer to a point capable of that level of sophistication, something that approaches general intelligence is a much tougher nut to crack.

Certainly, the advent of SDK for systems has helped dramatically increase the breadth of tasks that can be performed by robots like Boston Dynamics’ Spot, true artificial general intelligence is further off – if we ever get there.

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波士顿动力 Atlas AI 机器人 丰田研究院
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