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Destro AI的秘诀在于让机器人和人类达成共识Destro AI’s secret sauce is getting robots and humans on the same page

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根据初创公司 Destro 的说法,成功部署机器人的最佳方法其实根本不是从头开始制造机器人本身。这家公司周二低调宣布完成了 800 万美元的种子轮融资。

The best path to successfully deploying a robot is not building one at all, according to Destro, a startup that came out of stealth Tuesday with an $8 million seed round.

大多数新兴的机器人公司都致力于寻找合适的设备形态或开发能够驱动机器人执行任务的 AI 模型;而 Destro 则专注于构建一种人工智能系统,使机器人能够在物流环境中高效工作——同时该系统还会指导人类工作人员该做什么。

Most new robotics companies are focused on finding the right form factor or developing an AI model that can put metal hands to work. Destro has instead built an AI intelligence layer that lets robots work effectively in logistics settings, in part by also telling the human workers what to do.

“许多机器人公司都是从机器人工程师的角度出发,思考‘我能做出什么有趣的东西?’”Destro 的创始人 Manthan Pawar(一位行业资深人士)对 TechCrunch 说道,“我们之所以能在竞争中胜出,其中一个重要原因就是:我们本身并不是一家传统的机器人公司。”

“A lot of robotics companies started from robotics engineers [asking] ‘What cool things I can do?'”Destro founder Manthan Pawar, a veteran of the industry, told TechCrunch. “One of the biggest reasons we are winning against robotics companies is because we are not a robotics company.”

Pawar 在纽约大学坦登分校获得了机器人工程硕士学位,并在美国的供应链和机器人行业工作了大约八年。他解释说:“我们非常了解客户的需求,因此我们有信心在今年年底实现盈利。”韩国航运巨头 Yusen Logistics 的美国分公司负责自动化系统的 Richard Brunelle 曾帮助这家初创公司明确了发展方向。Yusen Logistics 在美国拥有约 30 座需要自动化设备的仓库,目前该公司正在推广诸如卸货机器人和自动清洁设备等新技术。

Pawar, who earned a master’s degree in robotics at NYU Tandon and has spent roughly eight years in the U.S. supply chain and robotics industry, explains that “our mindset is, we know our customers’ problems so damn well, and we are actually on path to be cash flow positive at the end of this year.”Richard Brunelle, the director of automation for the American logistics group at Yusen Logistics, a South Korean shipping giant, helped get the startup pointed in the right direction. Brunelle is responsible for automation across about 30 facilities in the U.S. There, Yusen uses fixed automation, like conveyors and sorters, but it is still piloting new technology like trailer-unloading robots and autonomous floor scrubbers.

Brunelle 的主要任务是寻找更多方法来应用这些技术;Yusen Logistics 的英国子公司已经建成了一个完全自动化的仓库,类似的计划也在美国正在推进中。当 Brunelle 遇到 Pawar 时,Destro 正在专注于另一个领域——即货物的分拣和包装工作(即将各种商品分类到单独的包装中)。

Finding more ways to put technology like that to work is Brunelle’s priority: Yusen’s UK subsidiary has built out a fully autonomous facility and now a similar plan is in the works in the U.S. When Brunelle met Pawar, Destro was focused on a different challenge in the space—picking and pack, the process by which various goods are sorted into single packages.

布吕内尔意识到,他面临的主要难题“本质上是同一个问题”。他的工作涉及越库作业:从一辆卡车上卸下货物,再将其分拣并混装到其他卡车上,由这些卡车最终将货物配送给客户。

Brunelle realized his primary challenge was “fundamentally the same problem.”His job involved cross-docking: unloading goods from one truck and sorting them into mixed loads for other trucks that make the final delivery to customers.

“我问Destro是否愿意调整其解决方案,使其适应越库作业环境,因为我认为当时还没有人在做这件事,”他告诉TechCrunch。“他们回去认真研究了一番,回来时十分兴奋。他们一致认为,没有人涉足这一领域,而他们可以修改工具来支持这种作业。从那以后,他们便全力投入其中。”Destro最初在Yusen位于太平洋西北地区的一处设施开展试点项目,使用三台由Miva Robotics制造、由Destro的Vision操作系统控制的推车搬运机器人。

该操作系统基于开放权重的视觉—语言—动作模型,能够解读摄像头图像和指令,从而控制机器人的移动。

“I asked whether Destro would be willing to adapt their solution to fit that cross-dock environment, because I don’t think anyone was doing that,” he told TechCrunch. “They went away, thought it through, and came back excited. They agreed nobody was addressing that space and that they could modify their tool to support it. Since then, they’ve gone all in.”Destro started with a pilot program at one Yusen facility in the Pacific northwest, using three cart-moving robots built by Miva Robotics and operated by Destro’s Vision operating system based on open-weight vision-language-action models (AI systems that interpret camera images and instructions to control a robot’s movements).

当人工 workers 将货物从一辆卡车卸到不同的推车中时,机器人会找到已经装满货物的推车,并将它们送往目的地。布吕内尔和帕瓦尔认为,提高效率的关键在于,人、推车和卡车全都由Destro的Mothership操作系统统一调度。

As human workers unload goods from one truck into different carts, the robots find the full loads and bring them to their destinations. What makes this efficient, in the estimation of Brunelle and Pawar, is that the human, the cart, and the trucks are all directed by Destro’s Mothership operating system.

“我们减少了这项作业对人工劳动的依赖,也让纸质单据彻底退出,”布吕内尔说。“现在的一切都已实现系统化。”这一点很重要,因为Yusen还与该领域另外两家知名机器人初创企业进行了接洽,但它们都无法融入Yusen的工作流程。其中一家提供的机器人只能在推车之间进行点对点移动,无法管理装货或卸货;另一家虽然提供车队管理工具,但所有调度工作仍需由人工完成。Destro胜出的原因在于,它能够对整个流程实施自主调度。

“We make that operation less labor intensive, and we get away from the paper,” Brunelle said. “Everything is now systematic.”That matters because Yusen also talked to two other big-name robot startups in this space, and neither could fit into their workflow. One offered a robot that could move the carts point-to-point but could not manage loading or unloading. The other came with fleet management tools, but required a person to handle all the orchestration. Destro won because of its ability to apply autonomous direction to the whole process.

如今,Destro正将最初的试点扩展至全面部署26台机器人,并在Yusen位于南加州的设施启动另一个由17台机器人参与的试点项目。Pawar计划把这套越库装卸工作流“复制粘贴”到数千家从事此类劳动的其他仓库。正是这项计划促使Base10 Partners和Bonfire Ventures领投Destro的种子轮融资,CoFound Partners也追加了投资。

Now, Destro is expanding its initial pilot to a full deployment of 26 robots and launching another pilot with 17 robots at Yusen’s facility in Southern California. Pawar plans to take this cross-dock loading workflow and “copy-paste” across thousands of other warehouses that handle this kind of labor. It’s that plan that convinced Base10 Partners and Bonfire Ventures to lead Destro’s seed round, with additional investment from CoFound Partners.

Destro在需要更高灵巧性和操控能力的工作流方面可能面临挑战。通用机器人本体和开源模型尚未证明自己能够胜任这些任务;此外,开发基础模型、超灵巧手或通用人形机器人的公司会否把这些工具提供给Destro这样的企业,而非试图亲自拿下这块业务,目前尚不明朗。

Destro may face a challenge with workflows that require more dexterity and manipulation. Generic robot bodies and open-source models haven’t proven capable at these tasks yet, and it’s not clear if companies building foundation models, ultra-dexterous hands, or general-purpose humanoids will make those tools available to a company like Destro rather than trying to capture that business themselves.

Pawar仍相信,他已经找到了整个技术栈中最容易产生价值的环节,公司也将能够利用正大量投入新型AI模型和机器人部件研发的科研资金。

Pawar remains confident that he’s found the part of the stack where value will accrue, and that his company will be able to take advantage of the research dollars pouring into new AI models and robot components.

“机器人是一个平台。各个层级的模型都是平台。但我们会在其外围构建一个配套框架,”Pawar告诉TechCrunch。“这个配套框架极其复杂,而且能够创造大量附加值;没有它,这些工作流根本不可能实现,对吧?”目前来看,与那些 futurism 机器人开发商竞争或许还不是问题。Brunelle表示,他尚未找到双足人形机器人的合适应用场景,不过正在评估一个采用轮式人形机器人的潜在试点项目。

“Robots are a platform. Every layer model is a platform. But we build a harness around it,” Pawar told TechCrunch. “That harness is just so complex and value-added that without that harness, these workflows are completely impossible, right?”For now, competing with futuristic robot-builders is probably not an issue. Brunelle says he has yet to find a good use case for a bipedal humanoid robot, though he is evaluating a potential pilot with a wheeled humanoid.

“归根结底,我们不会仅仅因为技术有意思,就把自动化设备引入建筑物,”布内勒说。“它必须解决实际的运营问题。如果它能让运营更加高效、稳定且灵活,那么最终受益的将是我们的客户。”几周后,德斯特罗将在TechCrunch Disrupt大会上推介这家“无机器人的机器人公司”。欢迎大家十月到场,除其他“战场”参赛企业外,也可亲眼看看德斯特罗。

“At the end of the day, we’re not putting automation into a building just because the technology is interesting,” Brunelle said. “It must solve a real operational problem. If it can make the operation more efficient, consistent, and flexible, that’s ultimately going to benefit our customers.”Destro will be making its case for the non-robot robot company at TechCrunch Disrupt in just a few weeks. Join us in October to see Destro in person, along with the rest of our Battlefield competitors.