Encord, a data infrastructure company for Physical AI, has released EMG-1, which it calls the largest open-source dataset pairing muscle signals with first-person video. Built with Wearable Devices, it holds more than 300 hours of recordings, over 30 times more than the next-largest public dataset of its kind. Wristbands read forearm muscle signals (electromyography, or EMG), showing what a hand is doing even when a camera cannot see it. Combining EMG with video cut fingertip tracking error by 25% compared with video alone, and by 35% during short gaps in visual tracking. The release launches Encord Data Labs, a research division. It also introduced Encord-REACH, a hand-tracking model that cut wrist tracking errors by 80% and joint errors by 65%. Researchers can download the dataset at encord.com/data-labs.

Encord bets on brain wave robot training to solve the physical AI data crisis. Brain wave robot training has arrived in a warehouse in San Leandro, California, and it looks like a Jenga game. Encord, a data-tooling startup that recently closed a $60 million Series C led by Wellington Management, is experimenting with headsets that measure brain activity while human operators disassemble block towers, pour coffee, and stack poker chips, all in the service of training robot models. The idea: that mental states, not just physical movements, might be the missing signal for physical AI. The headset in question was built by Zander Labs, a German-Dutch deep-tech company founded in 2016. Its hardware product, the Zypher Suite, is a mobile EEG system designed to monitor brain activity and provide real-time neuroadaptive data with local processing. Lucas Gehrke, a Zander neuroscientist supervising the Encord trial, says the volume of brain activity at any moment in a task reveals when a model needs to deploy its highest-effort processing, essentially flagging where the hard bits are. Encord's work with Zander is still a trial run. The plan is to build an initial brain wave-tagged data set, run it through customer robotics models, and evaluate whether it actually moves performance metrics before deciding whether to scale. That is a sensible way to frame a bet on genuinely unproven ground. Brain wave robot training gets serious, but the data problem is bigger. Vineeth Velmurugan, Encord's head of robot learning and a veteran of OpenAI's robot lab and warehouse-automation firm Berkshire Grey, describes brain wave work as the 'bleeding edge' of addressing the robotics data bottleneck. The bottleneck itself is less exotic: the physical world simply has not generated enough labelled manipulation data to train capable robot models at scale. 'The data simply does not exist,' Velmurugan says. He estimates it will take a data set roughly five times the size of YouTube's entire video corpus to break through. That figure helps explain why data generation has become a commercial business rather than a side project inside a research lab. Encord collects two main types of training data. The first is 'egocentric' video, captured by workers wearing head-mounted cameras, often augmented with extra camera angles and sensor readings. The second is data from leader-follower rigs, paired robotic arms where a human controls one and the other mimics it. When TechCrunch visited the facility, pilots were using these rigs to practise pouring coffee and stacking poker chips. Storage racks held fake flowers, plastic vegetables, kitty litter trays, bags of cables: the props list for teaching robots to handle household objects. Encord is also developing a forearm sensor that reads electrical signals in muscles, aiming to reconstruct a 3D model of hand position for moments when camera footage loses sight of the fingers. Each data set gets dense text annotation, 'right hand tightens bolt' style, to help language-model-based robot brains parse what is happening. Velmurugan estimates that kind of annotation is worth 100 times as much as raw egocentric footage for training specific tasks, and costs only 20 times more to produce. On paper, a good trade. In practice, 20 times more than near-zero is still a real number. The economics that make physical AI different from the LLM playbook. This is where the comparison between physical AI and large language models runs out of road. LLM builders scraped text from Stack Overflow and the rest of the open web for close to nothing. Physical training data has to be manufactured by people in warehouses with sensors on their forearms and, now, electrodes on their heads. The cost structure is categorically different. Encord's own growth suggests the market is real regardless of that friction. According to SiliconANGLE, the data volume on Encord's platform grew from just over 1 petabyte to more than 5 petabytes in the roughly 18 months before the Series C, and revenue increased more than 10 times over that same period. The Series C, which also drew in existing backers Y Combinator, CRV, N47, and Crane Venture Partners alongside new investors Bright Pixel and Isomer Capital, brings total funding to $110 million. Zander Labs, for its part, has broader ambitions than robot training experiments. The company signed a contract worth €30 million (approximately $32.9 million) with the German Agency for Innovation in Cybersecurity to develop neurotechnological prototypes under a project called NAFAS, which uses a passive brain-computer interface that does not require users to actively imagine actions. That contract runs until November 2027, according to MassDevice. Competition is forming. In June 2026, a rival physical-AI data startup called XDOF emerged from stealth with $70 million in funding from Thrive Capital, Spark Capital, a16z, Lux, and WndrCo. Founded in October 2024 by UC Berkeley researchers, XDOF already serves about 20 customers and released a 130,000-trajectory manipulation dataset with Berkeley's AI research lab, according to Business Model Analyst. The race to become the default supplier of physical training data is, apparently, fully on. Back in San Leandro, Andrew Ceja is rebuilding the Jenga tower and pulling it apart again. He previously worked at Scale, another AI data-annotation firm, before that at a waste-management company where he kept a robotic trash sorter running. Now he annotates his own brain waves for the benefit of robot models that do not yet exist at scale. 'It's something new every day!' he says. The brain wave data may or may not move the needle on model performance. The question Encord will answer first is whether the signal is real. The economics question comes after that. Marcus Hale has been filing general news for the better part of fifteen years. He started at a regional evening paper, moved to a mid-sized digital outlet covering UK news, and spent three years as a general assignment reporter before going freelance. He has covered inquests, council elections, infrastructure announcements, and the kind of stories that sit on page five but matter on page one. He writes about public services, housing, local government, and the institutional stories that take six months to develop and thirty seconds to read. He prefers facts to angles and considers that unfashionable. Marcus lives in Bristol. He still reads the local paper and thinks that makes him an endangered species. July 31, 2026 July 30, 2026

Brain waves: A new weapon for training AI robots. July 27, 2026 8 views In a warehouse in San Leandro, California, an employee sits wearing a helmet equipped with a camera and neural sensors, carefully pulling wooden blocks from a wobbling tower in full concentration. The scene looks ordinary at first glance, but it is in fact a next-generation experiment: measuring human brain waves with the goal of improving robots' ability to learn and understand the physical world around them. The data crisis: the real obstacle facing tomorrow's robots. The physical AI industry - the field concerned with building robotic systems capable of interacting with their surrounding environment - faces a fundamental challenge that has nothing to do with model design or processing power. The real bottleneck lies in the scarcity of real-world training data. Unlike large language models, which built their knowledge base from billions of web pages at minimal cost, robots require precise physical data that is difficult to collect or replicate digitally. UK-based Encord, a company specializing in data tools for training AI models, is tackling this crisis with a different approach: rather than simply managing existing data, it now manufactures data from scratch. Vineet Velmoorgan, Head of Robotic Machine Learning at Encord and a veteran of OpenAI's robotics lab, puts it plainly: "The data simply doesn't exist." Brain waves: data from a new layer. Encord is collaborating with German neuroscience firm Zander Labs to develop a helmet that measures the brain's electrical activity while human operators perform manual tasks. The idea goes beyond recording movements - it extends to capturing mental states such as error, surprise, and intent, converting them into an additional data layer that supplements traditional video footage. Lucas Gehrke, the neuroscientist overseeing the project, explains that brain activity levels at specific moments give models valuable cues about when to apply the highest levels of computational attention - which translates in practice to more efficient models that waste fewer resources. The experiment is still in its early stages; Encord plans to build a dataset annotated with brain wave data, then test its impact on the performance of actual robotic models before deciding whether to scale up. Multiple approaches to collecting real-world data. Encord's pipeline relies on two primary sources for generating training data: * Egocentric Video: Footage captured from the human operator's perspective via head-mounted cameras, supplemented by additional angles and complementary measurements, collected from factories at multiple locations around the world. * Leader-Follower Rigs: Paired robotic arms in which a human directly controls one arm while the other precisely mirrors its movements, used to capture data on fine-grained tasks such as pouring coffee and connecting cables. The team is also experimenting with a third emerging technique: forearm-mounted sensors that read electrical signals in the muscles, with the aim of building a full three-dimensional model of hand movement - something conventional video footage cannot achieve with sufficient accuracy. Cost: the decisive difference between robots and chatbots. Velmoorgan estimates that richly annotated data - such as "right hand tightening the screw" - is up to one hundred times more valuable for training than raw data, while costing only about twenty times more to produce. On paper, that's a winning equation. Yet "twenty times more" still means real money. This is the fundamental difference between physical AI and its language counterpart: language models built their knowledge wealth from mostly free internet data, while robot data must be generated hand by hand, trial by trial - radically changing the economics underlying the development of these models. Velmoorgan believes that reaching a true breakthrough requires a volume of data roughly equivalent to five times all the video content on YouTube. Strategic position: working with everyone at once. Encord's work with a broad spectrum of robotics companies - whose names Velmoorgan declines to reveal - affords the company a rare strategic vantage point on progress across the sector. It can observe which approaches prove most effective and which fall short, then leverage that accumulated knowledge for the benefit of all its clients. In a market where data is the rarest gem of all, this intermediary position amounts to a formidable competitive advantage. What is happening in the San Leandro warehouse is not merely data collection; it is an emerging model for an entire industry: the real-world data industry, which may prove to be the hidden engine driving the physical AI revolution in the years ahead. بقلم فريق دروب أيديا DROPIDEA. Dropidea hope this article has added real value to you. At DROPIDEA, Dropidea always strive to deliver high-quality content that helps you grow and evolve in the digital space. Follow Dropidea for more useful articles and guides. #الذكاء الاصطناعي الجسدي #الروبوتات #بيانات التدريب #موجات الدماغ