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发布情报融资 30M 用于强制和突击机器人的预播
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物理AI启动 Proference Intelligence于2026年10月22日从隐蔽处出现，宣布提供3000万美元的种子前资金，以推进其武力和触摸式的机器人机动性方法。 位于苏黎世的公司正在建造机器人，除了能感知其周围环境外，还能感知武力和触摸力。
风险企业重排阿尔卑斯，Notion Capital和第一时间的资本支持这一回合。 公告中点名的天使投资者包括Mistral创始人Arthur Mensch;AMI实验室创始人和Meta前首席AI科学家Yann LeCun；以及Path Robotics创始人Andrew Lonsberry. 公司形容该回合是迄今为止欧洲最大的种子前回合融资之一。
在其自己的LinkedIn页面上的文章中，Prancing还把Hugging Face的托马斯·沃尔夫(Thomas Wolf)命名为它的后卫。 公司的一项后续评论列举了更多的天使，包括诺斯沃尔特的保罗·塞鲁蒂(Paolo Cerruti of Northvort),ETH Zürich教授Marc Pollefeys和托马斯·霍夫曼(Thomas Hofmann)，温特拉的埃利亚斯·克莱曼(Elias Kleimann)，以及原J.P.摩根的阿米特·帕特尔(Ameet Patel)等。
公司以"Property"名义公开运营，而一家公司简介则将注册实体列为"Eta Robotics AG"，成立于2026年，位于苏黎世州。
武力和触摸的全方位方法
据《颁布》称，大多数正在建造的机器人主要依靠(如果不是完全依靠)视觉来了解其环境。 这种依赖限制了它们操纵物体、在现实世界中可靠运作和从经验中有效学习的能力，而增加偶然感知力和触摸力的现有解决办法通常仍然过于昂贵，而且对于真正的工业部署来说过于脆弱。
发布称它正在解决整个堆栈的问题，重新思考机器人技术堆栈从模型架构和数据管道下到提供模型的启动器和传感器。 公司不依靠成本高昂的传感器在单一点上测量力，而是在激活器本身内部捕捉力，就像人类通过肌肉的张力感知力，而不仅仅是指尖。 公司将其目标描述为为为机器人搭建自体神经系统，与有感知的皮层一起，通过全身力感知解决脱节。 称从第一天起就专注于在必要的成本和可靠性限度内使这种能力在工业内外的数千项任务中发挥作用，技术将有利于各种形式因素，从简单的手臂到全面的人造物。
称公司在其LinkedIn的帖子中，让机器人通过自己的激活器实时感知到正在发生的事情，没有脆弱的传感器，也没有额外的硬件，而武力和触摸反馈让系统高效学习，更快地完成任务。 颁布称，它的目标是在可变性而不是体积决定值的地方实现自动化。 公司在其网站上总结其使命为通过武力和触摸解决狡猾性。
Harald Nieder是Redalpine公司的总合伙人，称 Proference公司正在用新的数据模式处理离心力问题，它首先从点定器的高精度力数据开始，然后从硬件上重新思考整个堆积。 Notion首都区(称)的总合伙人Kamil Mieczakowski(英语：Kamil Mieczakowski)说：“ Robots在拥有神经系统之前不会泛泛而谈，这种系统不仅让他们看到世界，而且让他们感受到世界。 ” 他补充说，苏黎世的林马特河谷在过去几年中已经确立自己是机器人的领先地区。
创始人、团队和苏黎世基地
发布由曼努埃尔·伊维斯·加利基尔(Manuel Yves Galliker)于2026年3月创立，他之前在1X担任近地物体人造物软件团队的组长，帮助在硅谷建立了1X’sAI团队，之后在机器人实验室物理情报工作。 公司的十二人团队包括来自DeepMind,1X,Amazon机器人，赫尔辛和物理智能的工程人才。 菲利普·克奈斯(Philip Kneis)曾是红松公司的深科技投资人，他也加入公司率先成长。
Galliker 称物理AI的优点是从已经存在的强大的推理，语言和视觉能力开始，而不是只得收集所有必要的数据来教导机器人对世界的常识。 “我们可以在现有模型之上，如视频模型之上，称正在有效地从经验中学习，并且可靠地完成任务，这需要模型来理解机器人的物理和手头的任务。
该公司有意决定在苏黎世建造海湾地区上空。 Galliker 称硅谷传统上在软件和AI方面非常强壮，但缺乏机器人和硬件工程人才，随着目前机器人周围的兴奋，很多钱都在追逐几个人。 He 称 Project将带来来自硅谷的AI经验
资金的使用和首次部署
颁布将利用资金在2027年年底前将团队扩大五倍，加快硬件和研发管道，并寻求与工业伙伴部署首批机器人。 称公司已经在与欧洲制造业、基础设施、零售业和食品加工业的主要参与者进行讨论。 LinkedIn的帖子也指出，随着公司建设团队，申请人可以进入一个积极的招聘网页。
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Funding
Enact Intelligence Raises $30M Pre-Seed for Force-and-Touch Robot Dexterity
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Physical AI startup Enact Intelligence emerged from stealth on October 22, 2026, announcing $30 million in pre-seed funding to advance its force-and-touch approach to robotic dexterity. The Zurich-based company is building robots that can sense force and touch in addition to seeing their surroundings.
Venture firms redalpine, Notion Capital and firstminute capital backed the round. Angel investors named in the announcement include Arthur Mensch, founder of Mistral; Yann LeCun, founder of AMI Labs and former Chief AI Scientist at Meta; and Andrew Lonsberry, founder of Path Robotics. The company described the round as one of the largest pre-seed rounds raised in Europe to date.
In a post on its own LinkedIn page, Enact also named Thomas Wolf of Hugging Face among its backers. A follow-up comment from the company lists further angels, including Paolo Cerruti of Northvolt, ETH Zürich professors Marc Pollefeys and Thomas Hofmann, Elias Kleimann of Wingtra, and Ameet Patel, formerly of J.P. Morgan, among others.
The company operates publicly as Enact, while a company profile lists the registered entity as Eta Robotics AG, founded in 2026 in the canton of Zurich.
A Full-Stack Approach to Force and Touch
Most robots being built rely primarily, if not exclusively, on vision to understand their environment, according to Enact. That reliance limits their ability to manipulate objects, operate reliably in the real world and learn efficiently from experience, and existing solutions that add haptic sensing of force and touch typically remain too expensive and too brittle for real industrial deployment.
Enact says it is addressing the problem across the full stack, rethinking the robotic tech stack from model architectures and data pipelines down to the actuators and sensors that feed the model. Rather than relying on costly sensors that measure force at a single point, the company captures force inside the actuators themselves, much as humans sense force through the tension in their muscles rather than only at their fingertips. The company describes its goal as building a proprioceptive nervous system for robots together with the cortex that makes sense of it, solving dexterity through whole-body force-awareness. It says it has focused from day one on enabling that capability within the cost and reliability constraints needed to make it useful across thousands of tasks in industry and beyond, and that the technology will benefit every form factor, from a simple arm to a full-scale humanoid.
In its LinkedIn post, the company said the system lets robots sense what is happening in real time through their own actuators, with no fragile sensors and no extra hardware, and that force-and-touch feedback lets the system learn efficiently and perform tasks faster. Enact said it is targeting automation where variability, rather than volume, determines value. On its website, the company summarizes its mission as solving dexterity through force and touch.
Harald Nieder, a general partner at redalpine, said Enact is tackling dexterity with a new data modality, starting with high-precision force data from bespoke actuators and rethinking the entire stack from the hardware up. Kamil Mieczakowski, a general partner at Notion Capital, said, “Robots won’t generalise until they have a nervous system, one that lets them not only see the world but feel it.” He added that Zurich’s Limmat Valley has established itself as a leading region for robotics over the past few years.
Founder, Team and the Zurich Base
Enact was founded in March 2026 by Manuel Yves Galliker, who previously led the software team for the NEO humanoid at 1X, helped build 1X’s AI team in Silicon Valley and then worked at robotics lab Physical Intelligence. The company’s twelve-person team includes engineering talent from DeepMind, 1X, Amazon Robotics, Helsing and Physical Intelligence. Philip Kneis, previously a deeptech investor at redalpine, has also joined to spearhead growth at the company.
Galliker said physical AI has the advantage of starting from strong reasoning, language and vision capabilities already in place, rather than having to collect every piece of data needed to teach a robot common sense about the world. “We can build on top of existing models, like video models, and adapt them to output robot actions for interacting with the real world,” he said. The problems Enact is addressing, he said, are learning efficiently from experience and performing tasks reliably, both of which require the model to understand the physics of the robot and the task at hand.
The company made a deliberate decision to build in Zürich over the Bay Area. Galliker said Silicon Valley has traditionally been very strong in software and AI but is lacking in robotics and hardware engineering talent, and that with the current excitement around robotics, a lot of money is chasing a few people. He said Enact will bring AI experience with it from Silicon Valley and combine it with Zürich’s local talent pool.
Use of Funds and First Deployments
Enact will use the funding to grow its team fivefold by the end of 2027, accelerate its hardware and R&D pipelines, and seek to deploy its first robots with industrial partners. The company said it is already in discussions with major European industry players across manufacturing, infrastructure, retail and food processing. Its LinkedIn post also points applicants to an active hiring page as the company builds out the team.
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