One could be excused for confusing the Investor’s list on the Covariant.ai website with the all-star lineup for a top AI conference. The names include 2018 Turing Award winners Geoffrey Hinton and Yann LeCun, Google AI lead Jeff Dean, Director of the Stanford Artificial Intelligence Lab Fei-Fei Li and Berkeley Artificial Intelligence Research (BAIR) Lab Founding Co-Director Trevor Darrell.
可能会因为将Covariant.ai网站上的投资者名单与顶级AI会议的全明星阵容混淆而被原谅。 这些名字包括2018年图灵奖的获得者Geoffrey Hinton和Yann LeCun，谷歌AI主管Jeff Dean，斯坦福人工智能实验室主任李飞飞和伯克利人工智能研究(BAIR)实验室创始联合主任Trevor Darrell。
Covariant last month secured a US$40 million Series B funding round led by Index Ventures to push its total funding to US$67 million. The ever-astute Professor Hinton recently tweeted he wishes he’d invested a hundred times more.
Covariant上个月获得了由Index Ventures牵头的4000万美元B轮融资，将其总资金推至6700万美元。 这位永远精明的教授欣顿最近在推特上发文说，他希望自己能再投资一百倍。
从学术界到现实世界 (From academia to real world)
Berkeley-based Covariant is building a universal AI designed to enable robots to see, reason, and act in the real world. “Covariant was founded with a very strong research DNA. And in a sense, you can say the company is on a quest to solve the hot research challenge of how do you build general AI for robotics,” Chief Executive and Co-founder Peter Chen told Synced. Initially known as Embodied Intelligence, the company was co-founded in 2017 by esteemed UC Berkeley professor Pieter Abbeel and colleagues Peter Chen, Rocky Duan and Tianhao Zhangfrom UC Berkeley and OpenAI.
基于伯克利的Covariant正在构建一种通用AI，旨在使机器人能够在现实世界中看到，推理和行动。 “ Covariant成立时有着非常强大的研究DNA。 从某种意义上讲，您可以说该公司正在寻求解决如何构建用于机器人技术的通用AI的热门研究难题， ”首席执行官兼联合创始人Peter Chen告诉Synced 。 该公司最初被称为Embodied Intelligence，由知名的加州大学伯克利分校教授Pieter Abbeel及其同事Peter Chen，Rocky Duan和来自加州大学伯克利分校和OpenAI的Zhang Tianhao于2017年共同创立。
The launch came with a mandate both ambiguous and ambitious, based on the team’s extensive expertise in artificial intelligence, deep learning, and robotics: “Traditional robot programming required substantial time and expertise,” explained Abbeel in a seed founding round press release. “What we will provide is an AI layer that can be added to any existing robot, enabling robots to learn new skills rather than requiring explicit programming.”
基于团队在人工智能，深度学习和机器人领域的广泛专业知识，此次发布的任务既含糊又雄心勃勃：“传统的机器人编程需要大量的时间和专业知识，” Abbeel在种子创始回合新闻稿中解释道。 “ 我们将提供的AI层可以添加到任何现有的机器人中，从而使机器人能够学习新技能，而无需进行明确的编程。 ”
Three years later, Covariant’s marque product is its Covariant Brain solution, which delivers “universal AI for robots that can be applied to any use case or customer environment. Covariant robots learn general abilities such as robust 3D perception, physical affordances of objects, few-shot learning and real-time motion planning, which enables them to quickly learn to manipulate objects without being told what to do.”
三年后，Covariant的标志产品是Covariant Brain解决方案，该解决方案为可应用于任何用例或客户环境的机器人提供了“ 通用AI ” 。 协变机器人学习通用能力，例如强大的3D感知能力，物体的实际承受能力，几次射击学习和实时运动计划，这使它们能够快速学习操纵物体而无需告知要做的事情。”
From advanced research techniques to robust applications in the warehouse, Covariant has developed and rolled out its solutions with impressive speed and success. In a 2019 global competition organized by Swiss-based ABB Robotics, 20 AI startups were tasked with designing software for ABB robot arms, with the efforts evaluated on 26 real-world picking, packing, and sorting tasks. Covariant won the global competition and entered into a partnership with ABB to co-develop AI solutions to assist autonomous materials handling. The first such deployment was at Active Ants, a leading provider of e-commerce services for web businesses in the Netherlands.
从先进的研究技术到仓库中强大的应用程序，Covariant均以惊人的速度和成功开发并推出了其解决方案。 在总部位于瑞士的ABB Robotics举办的2019年全球竞赛中，有20家AI初创公司受命为ABB机械臂设计软件，并评估了26种现实世界中的拣选，包装和分类任务。 Covariant赢得了全球竞争，并 与ABB建立 了 合作伙伴关系 ，共同开发AI解决方案，以协助自动化物料搬运。 此类首次部署是在Active Ants上进行的，Active Ants是荷兰网络企业的领先电子商务服务提供商。
KNAPP, a world market leader in warehouse logistics and automation, has also been eyeing Covariant Brain. This March, KNAPP announced a partnership with Covariant to deploy and bring to market advanced AI Robotics solutions. Their first joint deployment was the Pick-It-Easy Robot at Obeta, a German electrical supply wholesaler located near Berlin. Jusuf Buzimkic, SVP of Engineering at KNAPP, enthusiastically endorsed the partnership: “We looked at every solution on the market, and Covariant was the clear winner. It can handle unlimited SKU types and works on challenging objects, including polybags, banded-apparel, transparent objects and blister packs. It also learns to pick new objects it’s never seen before and improves over time.” The New York Times reports the Pick-It-Easy Robot arm can handle more than 10,000 different items with better than 99 percent accuracy.
仓库物流和自动化领域的全球市场领导者KNAPP也一直在关注Covariant Brain。 今年3月，KNAPP宣布与Covariant 合作 ，以部署先进的AI机器人技术并将其推向市场。 他们的第一个联合部署是位于柏林附近的德国电力供应批发商Obeta的Pick-It-Easy机器人。 KNAPP工程高级副总裁Jusuf Buzimkic热情地支持了这一合作伙伴关系：“我们研究了市场上的每种解决方案，而Covariant无疑是赢家。 它可以处理无限制的SKU类型，并且可以处理 具有挑战性的物体，包括塑料袋，带状服装，透明物体和泡罩包装 。 它还学习选择前所未有的新对象，并随着时间的流逝而不断改进。 《 纽约时报》报道，Pick-It-Easy机器人手臂可以处理10,000多种不同的物品，准确性超过99％。
解决物流难题 (Tackling the tough part of logistics)
How have Covariant-powered robot arms so successfully positioned themselves in terms of logistics and funding? The phrase “pragmatic research” surfaced repeatedly in our interview with CEO Chen.
“There are no textbook answers to a lot of things that we are trying to build. You need to embrace the fact that you need to solve unsolved problems. Then you need to assemble a team that could do that cutting edge research and advance over the state-of-the-art.”
“对于我们试图构建的许多东西，没有教科书的答案。 您需要接受一个事实，那就是您需要解决未解决的问题。 然后，您需要组建一支可以进行前沿研究并超越最新技术的团队。 ”
“Much amazing research talent is concentrated in places like Google, Facebook, DeepMind and OpenAI. They tend to focus much more on long term fundamental research. So this is typically a very long term problem that they’re aiming towards. What we try to do is be very pragmatic. Be very specific about the real-world problems that we want to solve, and get very deep into the problems, get our hands dirty in order to understand. You can say it’s a strategy, but it’s more of a culture, more of a mindset that we have to build up as a company.”
“许多了不起的研究人才都集中在Google，Facebook，DeepMind和OpenAI等领域。 他们倾向于将重点更多地放在长期基础研究上。 因此，这通常是他们针对的非常长期的问题。 我们试图做的是非常务实的。 对我们要解决的现实问题非常具体，并深入了解这些问题，动手实践以便理解。 您可以说这是一种策略，但它更多地是一种文化，是我们作为一家公司必须树立的思维方式。”
Chen told Synced that Covariant heard from the market that although many have approached picking problems in logistics and offered automated solutions, there as yet aren’t many large-scale AI systems available. He stresses that the new era of AI robotics is different from traditional industrial automation: “Now, your robot needs to deal with variability. It needs to deal with randomness. It needs to deal with changes in your environment.”
Chen告诉Synced ，Covariant从市场上听说，尽管许多人已经解决了物流中的拣货问题并提供了自动化解决方案，但目前尚没有很多大型AI系统可用。 他强调说，人工智能机器人技术的新时代不同于传统的工业自动化： “现在，您的机器人需要处理可变性。 它需要处理随机性。 它需要应对环境的变化。 ”
It wasn’t a coincidence that Covariant chose to tackle the tougher part of logistics first. Abbeel told IEEE Spectrum that Covariant spent almost a year “talking with literally hundreds of different companies [in electronics manufacturing, car manufacturing, textiles, bio labs, construction, farming, hotels, eldercare, etc.] about how smarter robots could potentially make a difference for them. Over time, it became clear to us that manufacturing and logistics are the two spaces where there’s most demand now, and logistics especially is just hurting really hard for more automation.”
Covariant选择首先解决物流中更困难的部分并不是巧合。 Abbeel告诉IEEE Spectrum ，Covariant花费了将近一年的时间 “ 与数以百计的不同公司(在电子制造，汽车制造，纺织品，生物实验室，建筑，农业，酒店，养老院等)进行了交谈，探讨了更智能的机器人如何制造机器人。对他们来说有所不同。 随着时间的流逝，对我们来说很明显，制造和物流是目前需求最大的两个领域，尤其是物流对于真正实现更高的自动化正造成极大的伤害 。”
According to Statista, retail e-commerce sales worldwide have reached US$3.53 trillion and are expected to grow to US$6.54 trillion by 2022. Accelerating global growth in the e-commerce sector has seen industrial leaders shift focus to AI-powered robotics solutions across a wide range of applications, including logistics, warehousing, and package sorting.
The Covariant Brain solution has so far provided use cases in various warehouse operations, where robotic arm applications mainly include depalletizing, picking, and sorting. Depalletizer systems unload and handle palletized products of different shapes and forms — such as boxes, trays, cases, sheets, bags, pails, and pallets. Warehouse picking meanwhile is where items are picked from a fulfillment facility to complete customer orders, and is one of the most expensive and labour-heavy processes in warehouses. Sorting is another essential warehouse operation, involving identifying items and sending them to the correct bin or storage area. Warehouse sorting robots are typically equipped with conveyors, arms, cameras and sensors; and rely on specialized algorithms.
到目前为止，协变大脑解决方案已在各种仓库操作中提供了用例，其中机械手应用主要包括卸垛，拣选和分类 。 卸垛机系统卸载并处理不同形状和形式的托盘产品，例如箱子，托盘，箱子，薄片，袋子，桶和托盘。 同时，仓库拣货是从履行设施中拣选物品以完成客户订单的过程，并且是仓库中最昂贵且劳动最重的过程之一。 分类是仓库的另一项重要操作，涉及识别物品并将其发送到正确的垃圾箱或存储区域。 仓库分拣机器人通常配备有输送机，手臂，摄像机和传感器； 并依靠专门的算法
机器人通用AI平台 (Universal AI platform for robots)
Covariant’s mission is to build a universal AI platform that enables AI robotics work autonomously in the real world. Rather than customizing entire AI systems to fit in different use cases in different environments, Chen says what’s important is a system’s ability to generalize to something new. “The world is constantly changing. Once you deploy a system into the real world, you need to face a lot of that.”
Covariant的任务是建立一个通用的AI平台，使AI机器人技术能够在现实世界中自主工作。 Chen说，与其定制整个AI系统以使其适合不同环境中的不同用例，不如说重要的是系统具有将其推广到新事物的能力。 “世界在不断变化。 将系统部署到现实世界后，您需要面对很多。”
Covariant has built its business model to reflect its mission “A robotics system is not just the AI but also the robot itself, the surrounding, mechatronics equipment that you need to go with them. So the Covariant go-to-market model is what we call a development partner model. We will expose our Covariant Brain, this unified AI, as a platform, and our partners will develop on top of it.”
Covariant建立了自己的业务模型以反映其使命：“机器人系统不仅是AI，而且还包括机器人本身，与之配套的周围机电一体化设备。 因此，我们将协变推向市场模型称为开发合作伙伴模型。 我们将把Covariant Brain这种统一的AI作为平台公开，我们的合作伙伴将在此之上发展。”
Chen says Covariant’s partnership with industry leaders such as Knapp and ABB also points to where a universal AI for robots might go next. “How do we as an AI software company help scaling these kind of applications quickly? This is not a well-known path. You could say there are obstacles, but maybe, in a sense, they are challenges that you necessarily need to face when you create a new category.”
Chen说Covariant与Knapp和ABB等行业领导者的合作伙伴关系还指出了下一步可能会出现面向机器人的通用AI。 “作为一家AI软件公司，我们如何帮助快速扩展此类应用程序？ 这不是一条众所周知的路径。 您可以说存在障碍，但从某种意义上说，也许是当您创建新类别时必须要面对的挑战。”
Covariant plans to continue expanding into industries where robots are needed for repetitive tasks, such as food, healthcare, retail, parcels and manufacturing.
Journalist: Fangyu Cai | Editor: Michael Sarazen
记者 ：蔡芳玉| 编辑 ：迈克尔·萨拉森(Michael Sarazen)